This document proposes using subjective measures like profitability and loyalty, in addition to objective measures like support and confidence, for sequential pattern mining to identify potentially profitable customer groups.
It discusses limitations of existing sequential pattern mining approaches that focus only on frequency. The proposed approach incorporates emerging patterns, profit constraints, total monetary value, compactness, and recency to identify sequential patterns and customer segments that are truly useful for businesses.
Simulation studies on real-world datasets show the proposed approach using multiple measures outperforms existing methods in terms of runtime, memory usage, and ability to find meaningful patterns for targeting potential customers. Evaluation of various interestingness measures is also discussed.
IRJET- Customer Buying Prediction using Machine-Learning Techniques: A SurveyIRJET Journal
1) The document discusses using machine learning techniques to predict customer purchasing and churn based on their personal and behavioral data.
2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
This document provides a project report on studying consumer behavior for purchasing mobile handsets in Pune, India. The report includes details of the research methodology used such as objectives of the study, scope, sample design involving 200 mobile users, and data collection methods including questionnaires and personal interviews. It also discusses consumer buying behavior processes involving problem identification, information search, evaluation of alternatives, purchase decision making, and post-purchase behavior. The executive summary provides an overview of the key findings from the study.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
A Two Stage Classification Model for Call Center Purchase PredictionTELKOMNIKA JOURNAL
In call center [1] product recommendation field, call center as an organization between users and telecom operator, doesn’t have permission to access users’ specific information and the detailed products information. Accordingly, rule-based selection method is common used to predict user purchase behavior by the call center. Unfortunately, rule-based approach not only ignores the user’s previous behavior information entirely, and it is difficult to make use of the existing interaction records between users and products. Consequently, it will not get desired results if we just use the basic selection method to predict user purchase behavior directly, because the problem is that the features straightly extracted from the interaction data records are limited. In order to solve the problem above, this paper proposes a two-stage algorithm that based on K-Means Clustering Algorithm [2] and SVM [3, 4] Classification Algorithm. Firstly, we get the potential category information of products by K-Means Clustering Algorithm, and then use SVM Classification Model to predict users purchasing behavior. This two-stage prediction model not only solves the feature shortage problem, but also gives full consideration to the potential features between users and product categories, which can help us to gain significant performance in call center product recommendation field.
Customer segmentation for a mobile telecommunications company based on servic...Shohin Aheleroff
Competition between the mobile operators is becoming more based on subscriber’s behavior. In order to improve mobile operator’s competitiveness and customer value, several data mining technologies can be used.Most telecommunications carriers cluster their mobile customers by billing system data. This paper discusses how to cluster mobile customers based on their call detail records and analyze their consumer behaviors.
Telecommunication Analysis (3 use-cases) with IBM watson analyticssheetal sharma
1. The telecommunications company is concerned about customer churn and needs to understand the factors influencing why customers are leaving.
2. The analysis found that the two year contract has very few current customers and is influencing churn. Focusing on modifying the two year contract and its rate plans could increase customers.
3. For cross-selling and up-selling, the analysis found that prepaid customers are not using services broadly. Payment methods should be focused on and policies added to promote the company, such as discounts for cash payments or collaborating with banks.
- A marketing research project was conducted to analyze factors affecting stagnant sales at Retail House, a 50-year old hosiery store in IIT Kanpur.
- Data was collected through an online/printed questionnaire and analyzed using chi-square tests, t-tests, and discriminant analysis.
- Results showed that footfall at the shopping complex was associated with footfall at Retail House, and advertisement was associated with increased visitation. However, parking availability and distance of residence were not associated with shopping preferences. Customer turnaround time was also found to be less than 5 minutes, indicating efficient staff.
1. The document discusses identifying and profiling high value business customers in order to develop targeted promotions to increase average revenue per user (ARPU) and profitability.
2. It examines how to assess customer relationship management (CRM) systems used to collect customer data and determine customer value through segmentation and clustering.
3. Examples are provided of tailored promotions that were developed for high value business customers based on their needs and behaviors.
IRJET- Customer Buying Prediction using Machine-Learning Techniques: A SurveyIRJET Journal
1) The document discusses using machine learning techniques to predict customer purchasing and churn based on their personal and behavioral data.
2) It reviews several machine learning algorithms that have been used for prediction, including random forest, logistic regression, naive bayes, and support vector machines.
3) Deep learning techniques are also discussed, including the use of convolutional neural networks to reveal hidden patterns in customer data and predict purchases and churn.
This document provides a project report on studying consumer behavior for purchasing mobile handsets in Pune, India. The report includes details of the research methodology used such as objectives of the study, scope, sample design involving 200 mobile users, and data collection methods including questionnaires and personal interviews. It also discusses consumer buying behavior processes involving problem identification, information search, evaluation of alternatives, purchase decision making, and post-purchase behavior. The executive summary provides an overview of the key findings from the study.
IRJET - Customer Churn Analysis in Telecom IndustryIRJET Journal
This document discusses using machine learning techniques like logistic regression to analyze customer data and predict customer churn in the telecom industry. It proposes a system to build a churn prediction model using logistic regression on historical customer data to identify high-risk customers. The system would have options to view results, perform training and testing on new data, and analyze performance. It would also include a recommender system to recommend suitable plans for identified churn customers based on their usage patterns. The results show the model can predict churn with 80% accuracy and identify similar customers who may also churn.
A Two Stage Classification Model for Call Center Purchase PredictionTELKOMNIKA JOURNAL
In call center [1] product recommendation field, call center as an organization between users and telecom operator, doesn’t have permission to access users’ specific information and the detailed products information. Accordingly, rule-based selection method is common used to predict user purchase behavior by the call center. Unfortunately, rule-based approach not only ignores the user’s previous behavior information entirely, and it is difficult to make use of the existing interaction records between users and products. Consequently, it will not get desired results if we just use the basic selection method to predict user purchase behavior directly, because the problem is that the features straightly extracted from the interaction data records are limited. In order to solve the problem above, this paper proposes a two-stage algorithm that based on K-Means Clustering Algorithm [2] and SVM [3, 4] Classification Algorithm. Firstly, we get the potential category information of products by K-Means Clustering Algorithm, and then use SVM Classification Model to predict users purchasing behavior. This two-stage prediction model not only solves the feature shortage problem, but also gives full consideration to the potential features between users and product categories, which can help us to gain significant performance in call center product recommendation field.
Customer segmentation for a mobile telecommunications company based on servic...Shohin Aheleroff
Competition between the mobile operators is becoming more based on subscriber’s behavior. In order to improve mobile operator’s competitiveness and customer value, several data mining technologies can be used.Most telecommunications carriers cluster their mobile customers by billing system data. This paper discusses how to cluster mobile customers based on their call detail records and analyze their consumer behaviors.
Telecommunication Analysis (3 use-cases) with IBM watson analyticssheetal sharma
1. The telecommunications company is concerned about customer churn and needs to understand the factors influencing why customers are leaving.
2. The analysis found that the two year contract has very few current customers and is influencing churn. Focusing on modifying the two year contract and its rate plans could increase customers.
3. For cross-selling and up-selling, the analysis found that prepaid customers are not using services broadly. Payment methods should be focused on and policies added to promote the company, such as discounts for cash payments or collaborating with banks.
- A marketing research project was conducted to analyze factors affecting stagnant sales at Retail House, a 50-year old hosiery store in IIT Kanpur.
- Data was collected through an online/printed questionnaire and analyzed using chi-square tests, t-tests, and discriminant analysis.
- Results showed that footfall at the shopping complex was associated with footfall at Retail House, and advertisement was associated with increased visitation. However, parking availability and distance of residence were not associated with shopping preferences. Customer turnaround time was also found to be less than 5 minutes, indicating efficient staff.
1. The document discusses identifying and profiling high value business customers in order to develop targeted promotions to increase average revenue per user (ARPU) and profitability.
2. It examines how to assess customer relationship management (CRM) systems used to collect customer data and determine customer value through segmentation and clustering.
3. Examples are provided of tailored promotions that were developed for high value business customers based on their needs and behaviors.
Demand forecasting involves estimating future demand for a product or service. There are two main approaches: obtaining expert opinions or consumer surveys, or using past sales data through statistical techniques. Simple survey methods include expert opinion polls, the Delphi technique to reach consensus among experts, and consumer surveys using complete or sample enumeration. More complex statistical methods include time series analysis to identify trends, using leading economic indicators to forecast changes, and correlation/regression analysis to determine relationships between demand and influencing factors like price, income, and advertising. The most sophisticated method is simultaneous equation modeling or developing an econometric model of an entire economy.
This document discusses predicting customer churn in the telecom industry. It outlines collecting customer data from Kaggle on services used, account details, and demographics. Exploratory data analysis finds recent and higher spending customers more likely to churn. Feature selection and encoding is done before imbalanced data handling with SMOTE tomek. Various classifiers are tested with random forest performing best with an AUC of 0.834. Partial dependence plots and SHAP values are used to explain the model. Finally, a web app is created and deployed on Github to predict churn probabilities and help telecom companies reduce customer churn.
This document lists over 50 potential marketing project topics across various industries including luxury brands, retail, FMCG, financial services, consumer electronics, apparel, real estate, logistics, health and wellness, and agriculture. The topics focus on areas like branding, consumer behavior, distribution networks, promotional strategies, customer relationship management, and more. They provide opportunities to study marketing approaches, evaluate effectiveness, and analyze trends in different sectors.
Revenue management is a business strategy that optimizes profitability by varying prices based on demand factors. It originated in the airline industry in the 1970s when American Airlines introduced yield management to gain an advantage over competitors. Revenue management has since been adopted by hotels and other industries to increase revenues 3-6% or more by using technology to set consistent, data-driven pricing. While computer systems help, revenue management requires organizational cultural changes and coordination across departments to be most effective.
This document summarizes key points from Chapter 10 on relationship marketing, information technology, and sales forecasting. It discusses how relationship marketing focuses on developing long-term links with customers for mutual benefit. Information technology enables collecting customer data and using it to target customers more efficiently. Sales forecasting methods include executive judgement, surveys, time series analysis, and market tests to estimate a company's expected sales over different periods.
The document discusses how companies can implement next best offer strategies using customer data and signals. It describes how customers' purchasing behaviors have become more complex, influenced by various online sources. It then outlines how SAS software can help companies analyze customer data and behaviors to generate targeted, personalized offers at optimal times through real-time decisioning across all channels. Case studies show how US Bank improved sales and increased customer value using next best offer strategies based on signal and event analysis.
Marketing organizations are under pressure to improve their return on investment. Traditional outbound marketing tactics are no longer sufficient and often provide irrelevant experiences to customers. A Next-Best-Action approach uses decision management and analytics to determine the right message, time, and channel for each customer interaction. Pega's marketing solution dynamically manages cross-channel conversations through the customer lifecycle to optimize lifetime value.
CRM Implementation in Indian Telecom Industry – Evaluating the Effectiveness ...Waqas Tariq
With the liberalization and internationalization in telecommunication, service quality has become an important means of differentiation and path to achieve business success. Such differentiation based on service quality is seen as a key source of competitiveness for many Indian firms and hence have implications for leadership in such organizations. Faced with a growing market and increasing competition, companies in the telecom business are adopting to new technological imperatives in order to outperform their competitors. These companies adapt continuously to the dynamic environment so as to survive competition. The emphasis here lies in identifying critical value adding processes and redesigning them to become customer centric. One such approach in the adoption of an IT to move towards customers is the Customer Relationship Management (CRM). The Indian Mobile Service Providers are using CRM extensively to identify the needs of the customers and stretching out ways and means to satisfy them. In this context, it is absolutely essential to study the effectiveness of the CRM being practiced by the mobile service providers. This study specifically analyses the extent to which CRM is being practiced by the mobile service providers, and identifies the effect of the service quality of the mobile service providers on the Customer Loyalty. As CRM focuses on being customer centric, it becomes essential to measure the effectiveness of CRM in terms of the degree to which the customers are advocates of the mobile service provider as well as to measure the degree to which they participate in the cross selling and up selling of the various products and services of the provider. To evaluate the effectiveness, there are lots of quantitative techniques available and some work in this area has already been done. But there is a dearth of literature focusing on the relative efficiency. One advanced operations research technique which evaluates the relative efficiency is the Frontier Analysis or Data Envelopment Analysis (DEA). This paper attempts to use Data Envelopment Analysis to assess the effectiveness of Mobile Service Providers, specifically a set of the providers offering services in Chennai, Tamil Nadu, India. The research has identified a set of input and output parameters for each Service Provider, from which the efficient frontiers (DMUs) are determined. The relative efficiency of the Service Providers are measured with respect to the efficient frontier and then analyzed. Detailed recommendations are set forth, for appropriate interventions to address the specific gaps identified through the gaps analysis. The analysis further provides useful information and opens up new avenues for future research.
Service quality and customer satisfaction in the banking industryPatrick Sweet
This document discusses a study on the relationship between service quality and customer satisfaction in the Ghanaian banking industry, using Ghana Commercial Bank as a case study. It provides background on the importance of service quality and customer satisfaction in banking. The study aims to examine how the five dimensions of service quality (tangibles, reliability, responsiveness, assurance, and empathy) impact customer satisfaction. It also aims to understand how customers would rate these dimensions in terms of importance and need for improvement. The document reviews relevant literature on service quality models and dimensions. It describes the methodology used in the study, which assessed customer expectations and perceptions of service quality at three Ghana Commercial Bank branches using the SERVQUAL instrument.
The document is an assignment for a Management Information Systems course. It includes 5 questions related to MIS concepts.
1) The first question defines MIS, lists its characteristics and functions. It also provides disadvantages of MIS such as being highly sensitive and requiring constant monitoring.
2) The second question explains knowledge-based systems and decision support systems (DSS), providing an example of how DSS can be used. It also defines online analytical processing (OLAP).
3) The third question discusses value chain analysis, business process reengineering (BPR), and how data warehousing and data mining are useful for MIS.
4) The fourth question explains data flow diagrams (DFD) and data dictionaries
IRJET- Aspect based Sentiment Analysis on Financial Data using Transferred Le...IRJET Journal
This document presents an approach for aspect classification and sentiment prediction on financial data using transferred learning with BERT and regression models. The authors fine-tune BERT for aspect classification and use linear support vector regression for sentiment prediction, achieving an F1-score of 0.46-0.41 for aspect classification and MSE of 0.36-0.13 for sentiment prediction on the test data. They conclude BERT transfer learning is effective for this task and future work could explore other models like XLNet and larger datasets.
This document outlines a business research proposal for Parry Sugar Industries India Limited. It discusses conducting qualitative and quantitative research to determine the best advertising campaign for Parry. The research would involve desk research, focus groups, interviews, and projective techniques to understand consumers' views and decide on the most beneficial advertising approach. The research aims to consider costs and perspectives from households, workers, government agencies, and distributors.
This document contains a summary of Sandeep Kumar's professional experience and qualifications. He has an MBA in Marketing and is currently working as a Transaction Banking Relationship Manager at Axis Bank. Previously he completed projects related to modulation control of multi-level inverters and consumer research for packaging and features of coffee and watch brands. He is seeking a challenging role utilizing his experience in corporate banking.
Data mining and analysis of customer churn datasetRohan Choksi
The document discusses a study conducted by a mobile phone company to analyze factors related to customer churn. The company provided a dataset of 3,332 customer records to build a neural network model that can predict which customers are likely to switch providers. Examining the data showed that increased usage of night, evening, and day minutes, as well as more customer service calls, correlated with higher churn. International calling plans also had a major impact on churn rates. The model achieved a misclassification rate of 7.11% and identified key variables for the company to address to reduce churn, such as international call pricing and infrastructure issues.
Banks are increasingly targeting the large and profitable small- and medium-business (SMB) market but struggle to attract, retain, and serve SMB customers due to a lack of understanding of their needs. Accenture helps banks gain insights into SMB customers through analytics to reduce costs and risks associated with growing their SMB business. Accenture pairs analytics capabilities with technologies to create a rich 360-degree view of each SMB customer and tailor interactions accordingly to drive profitable growth.
The Fallacy of the Net Promoter Score: Customer Loyalty Predictive ModelIresha Anderson , M.S
This paper's objective was to recognize the fallacies of Net Promoter Score and propose an alternative customer loyalty model using big data techniques. The proposed model assesses and predicts customer loyalty using attitudinal, behavioral and demographic data.
This document summarizes a project that analyzed sentiment about three hatchback car brands (Chevrolet Spark, Ford Fiesta, Fiat Punto) from Twitter data. Over 250 tweets about the brands were read into R and preprocessed to extract meaningful insights. A sentiment analysis model was developed using keyword dictionaries and sentiment scores to analyze sentiment for each brand on a -2 to +5 scale. The results showed Fiat Punto with the highest positive sentiment of 40% due to its look and feel and sporty features. Chevrolet Spark scored better than Ford Fiesta but both brands need more attention in the market. The analysis provides business benefits to help brands improve products and target different customer segments.
This document discusses data science and provides examples of how data science has been applied in various industries. It defines key components of data science including data acquisition, data munging, math/statistics/data mining, and data visualization. It then provides several case studies showing how data science has been used to develop customer segmentation models, create scored customer databases, conduct online customer panels, model customer preferences, and conduct structural equation modeling. The case studies demonstrate how data science can help target customers, increase product usage, and inform strategic business decisions.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
DEMOGRAPHIC DIVISION OF A MART BY APPLYING CLUSTERING TECHNIQUESIRJET Journal
This document discusses demographic segmentation of customers at a mart by applying clustering techniques. It begins with an abstract that outlines the goal of employing advanced techniques like machine learning to target customer needs and increase sales. The introduction provides context on the increasing competitiveness of business and need for customer segmentation. The literature review summarizes several papers on topics like using machine learning for customer segmentation, comparing clustering algorithms on retail data, and dividing bank customers into clusters. The implementation section outlines the steps taken - data collection, cleaning, applying K-Means and agglomerative clustering, and exploratory data analysis. The proposed system aims to recognize the current customer situation, consolidate prior work, discover customer-attribute relationships, perform unsupervised clustering analysis and model evaluation,
Demand forecasting involves estimating future demand for a product or service. There are two main approaches: obtaining expert opinions or consumer surveys, or using past sales data through statistical techniques. Simple survey methods include expert opinion polls, the Delphi technique to reach consensus among experts, and consumer surveys using complete or sample enumeration. More complex statistical methods include time series analysis to identify trends, using leading economic indicators to forecast changes, and correlation/regression analysis to determine relationships between demand and influencing factors like price, income, and advertising. The most sophisticated method is simultaneous equation modeling or developing an econometric model of an entire economy.
This document discusses predicting customer churn in the telecom industry. It outlines collecting customer data from Kaggle on services used, account details, and demographics. Exploratory data analysis finds recent and higher spending customers more likely to churn. Feature selection and encoding is done before imbalanced data handling with SMOTE tomek. Various classifiers are tested with random forest performing best with an AUC of 0.834. Partial dependence plots and SHAP values are used to explain the model. Finally, a web app is created and deployed on Github to predict churn probabilities and help telecom companies reduce customer churn.
This document lists over 50 potential marketing project topics across various industries including luxury brands, retail, FMCG, financial services, consumer electronics, apparel, real estate, logistics, health and wellness, and agriculture. The topics focus on areas like branding, consumer behavior, distribution networks, promotional strategies, customer relationship management, and more. They provide opportunities to study marketing approaches, evaluate effectiveness, and analyze trends in different sectors.
Revenue management is a business strategy that optimizes profitability by varying prices based on demand factors. It originated in the airline industry in the 1970s when American Airlines introduced yield management to gain an advantage over competitors. Revenue management has since been adopted by hotels and other industries to increase revenues 3-6% or more by using technology to set consistent, data-driven pricing. While computer systems help, revenue management requires organizational cultural changes and coordination across departments to be most effective.
This document summarizes key points from Chapter 10 on relationship marketing, information technology, and sales forecasting. It discusses how relationship marketing focuses on developing long-term links with customers for mutual benefit. Information technology enables collecting customer data and using it to target customers more efficiently. Sales forecasting methods include executive judgement, surveys, time series analysis, and market tests to estimate a company's expected sales over different periods.
The document discusses how companies can implement next best offer strategies using customer data and signals. It describes how customers' purchasing behaviors have become more complex, influenced by various online sources. It then outlines how SAS software can help companies analyze customer data and behaviors to generate targeted, personalized offers at optimal times through real-time decisioning across all channels. Case studies show how US Bank improved sales and increased customer value using next best offer strategies based on signal and event analysis.
Marketing organizations are under pressure to improve their return on investment. Traditional outbound marketing tactics are no longer sufficient and often provide irrelevant experiences to customers. A Next-Best-Action approach uses decision management and analytics to determine the right message, time, and channel for each customer interaction. Pega's marketing solution dynamically manages cross-channel conversations through the customer lifecycle to optimize lifetime value.
CRM Implementation in Indian Telecom Industry – Evaluating the Effectiveness ...Waqas Tariq
With the liberalization and internationalization in telecommunication, service quality has become an important means of differentiation and path to achieve business success. Such differentiation based on service quality is seen as a key source of competitiveness for many Indian firms and hence have implications for leadership in such organizations. Faced with a growing market and increasing competition, companies in the telecom business are adopting to new technological imperatives in order to outperform their competitors. These companies adapt continuously to the dynamic environment so as to survive competition. The emphasis here lies in identifying critical value adding processes and redesigning them to become customer centric. One such approach in the adoption of an IT to move towards customers is the Customer Relationship Management (CRM). The Indian Mobile Service Providers are using CRM extensively to identify the needs of the customers and stretching out ways and means to satisfy them. In this context, it is absolutely essential to study the effectiveness of the CRM being practiced by the mobile service providers. This study specifically analyses the extent to which CRM is being practiced by the mobile service providers, and identifies the effect of the service quality of the mobile service providers on the Customer Loyalty. As CRM focuses on being customer centric, it becomes essential to measure the effectiveness of CRM in terms of the degree to which the customers are advocates of the mobile service provider as well as to measure the degree to which they participate in the cross selling and up selling of the various products and services of the provider. To evaluate the effectiveness, there are lots of quantitative techniques available and some work in this area has already been done. But there is a dearth of literature focusing on the relative efficiency. One advanced operations research technique which evaluates the relative efficiency is the Frontier Analysis or Data Envelopment Analysis (DEA). This paper attempts to use Data Envelopment Analysis to assess the effectiveness of Mobile Service Providers, specifically a set of the providers offering services in Chennai, Tamil Nadu, India. The research has identified a set of input and output parameters for each Service Provider, from which the efficient frontiers (DMUs) are determined. The relative efficiency of the Service Providers are measured with respect to the efficient frontier and then analyzed. Detailed recommendations are set forth, for appropriate interventions to address the specific gaps identified through the gaps analysis. The analysis further provides useful information and opens up new avenues for future research.
Service quality and customer satisfaction in the banking industryPatrick Sweet
This document discusses a study on the relationship between service quality and customer satisfaction in the Ghanaian banking industry, using Ghana Commercial Bank as a case study. It provides background on the importance of service quality and customer satisfaction in banking. The study aims to examine how the five dimensions of service quality (tangibles, reliability, responsiveness, assurance, and empathy) impact customer satisfaction. It also aims to understand how customers would rate these dimensions in terms of importance and need for improvement. The document reviews relevant literature on service quality models and dimensions. It describes the methodology used in the study, which assessed customer expectations and perceptions of service quality at three Ghana Commercial Bank branches using the SERVQUAL instrument.
The document is an assignment for a Management Information Systems course. It includes 5 questions related to MIS concepts.
1) The first question defines MIS, lists its characteristics and functions. It also provides disadvantages of MIS such as being highly sensitive and requiring constant monitoring.
2) The second question explains knowledge-based systems and decision support systems (DSS), providing an example of how DSS can be used. It also defines online analytical processing (OLAP).
3) The third question discusses value chain analysis, business process reengineering (BPR), and how data warehousing and data mining are useful for MIS.
4) The fourth question explains data flow diagrams (DFD) and data dictionaries
IRJET- Aspect based Sentiment Analysis on Financial Data using Transferred Le...IRJET Journal
This document presents an approach for aspect classification and sentiment prediction on financial data using transferred learning with BERT and regression models. The authors fine-tune BERT for aspect classification and use linear support vector regression for sentiment prediction, achieving an F1-score of 0.46-0.41 for aspect classification and MSE of 0.36-0.13 for sentiment prediction on the test data. They conclude BERT transfer learning is effective for this task and future work could explore other models like XLNet and larger datasets.
This document outlines a business research proposal for Parry Sugar Industries India Limited. It discusses conducting qualitative and quantitative research to determine the best advertising campaign for Parry. The research would involve desk research, focus groups, interviews, and projective techniques to understand consumers' views and decide on the most beneficial advertising approach. The research aims to consider costs and perspectives from households, workers, government agencies, and distributors.
This document contains a summary of Sandeep Kumar's professional experience and qualifications. He has an MBA in Marketing and is currently working as a Transaction Banking Relationship Manager at Axis Bank. Previously he completed projects related to modulation control of multi-level inverters and consumer research for packaging and features of coffee and watch brands. He is seeking a challenging role utilizing his experience in corporate banking.
Data mining and analysis of customer churn datasetRohan Choksi
The document discusses a study conducted by a mobile phone company to analyze factors related to customer churn. The company provided a dataset of 3,332 customer records to build a neural network model that can predict which customers are likely to switch providers. Examining the data showed that increased usage of night, evening, and day minutes, as well as more customer service calls, correlated with higher churn. International calling plans also had a major impact on churn rates. The model achieved a misclassification rate of 7.11% and identified key variables for the company to address to reduce churn, such as international call pricing and infrastructure issues.
Banks are increasingly targeting the large and profitable small- and medium-business (SMB) market but struggle to attract, retain, and serve SMB customers due to a lack of understanding of their needs. Accenture helps banks gain insights into SMB customers through analytics to reduce costs and risks associated with growing their SMB business. Accenture pairs analytics capabilities with technologies to create a rich 360-degree view of each SMB customer and tailor interactions accordingly to drive profitable growth.
The Fallacy of the Net Promoter Score: Customer Loyalty Predictive ModelIresha Anderson , M.S
This paper's objective was to recognize the fallacies of Net Promoter Score and propose an alternative customer loyalty model using big data techniques. The proposed model assesses and predicts customer loyalty using attitudinal, behavioral and demographic data.
This document summarizes a project that analyzed sentiment about three hatchback car brands (Chevrolet Spark, Ford Fiesta, Fiat Punto) from Twitter data. Over 250 tweets about the brands were read into R and preprocessed to extract meaningful insights. A sentiment analysis model was developed using keyword dictionaries and sentiment scores to analyze sentiment for each brand on a -2 to +5 scale. The results showed Fiat Punto with the highest positive sentiment of 40% due to its look and feel and sporty features. Chevrolet Spark scored better than Ford Fiesta but both brands need more attention in the market. The analysis provides business benefits to help brands improve products and target different customer segments.
This document discusses data science and provides examples of how data science has been applied in various industries. It defines key components of data science including data acquisition, data munging, math/statistics/data mining, and data visualization. It then provides several case studies showing how data science has been used to develop customer segmentation models, create scored customer databases, conduct online customer panels, model customer preferences, and conduct structural equation modeling. The case studies demonstrate how data science can help target customers, increase product usage, and inform strategic business decisions.
Customer Clustering Based on Customer Purchasing Sequence DataIJERA Editor
Customer clustering has become a priority for enterprises because of the importance of customer relationship management. Customer clustering can improve understanding of the composition and characteristics of customers, thereby enabling the creation of appropriate marketing strategies for each customer group. Previously, different customer clustering approaches have been proposed according to data type, namely customer profile data, customer value data, customer transaction data, and customer purchasing sequence data. This paper considers the customer clustering problem in the context of customer purchasing sequence data. However, two major aspects distinguish this paper from past research: (1) in our model, a customer sequence contains itemsets, which is a more realistic configuration than previous models, which assume a customer sequence would merely consist of items; and (2) in our model, a customer may belong to multiple clusters or no cluster, whereas in existing models a customer is limited to only one cluster. The second difference implies that each cluster discovered using our model represents a crucial type of customer behavior and that a customer can exhibit several types of behavior simultaneously. Finally, extensive experiments are conducted through a retail data set, and the results show that the clusters obtained by our model can provide more accurate descriptions of customer purchasing behaviors.
Using Data Mining Techniques in Customer SegmentationIJERA Editor
Data mining plays important role in marketing and is quite new. Although this field expands rapidly, data mining is still foreign issue for many marketers who trust only their experiences. Data mining techniques cannot substitute the significant role of domain experts and their business knowledge. In the other words, data mining algorithms are powerful but cannot effectively work without the active support of business experts. We can gain useful results by combining these techniques and business expertise. For instance ability of a data mining technique can be substantially increased by combining person experience in the field or information of business can be integrated into a data mining model to build a more successful result. Moreover, these results should always be evaluated by business experts. Thus, business knowledge can help and enrich the data mining results. On the other hand, data mining techniques can extract patterns that even the most experienced business people may have missed. In conclusion, the combination of business domain expertise with the power of data mining techniques can help organizations gain a competitive advantage in their efforts to optimize customer management. Clustering algorithms, a group of data mining technique, is one of most common used way to segment data set according to their similarities. This paper focuses on the topic of customer segmentation using data mining techniques. In the other words, we theoretically discuss about customer relationship management and then utilize couple of data mining algorithm specially clustering techniques for customer segmentation. We concentrated on behavioral segmentation.
DEMOGRAPHIC DIVISION OF A MART BY APPLYING CLUSTERING TECHNIQUESIRJET Journal
This document discusses demographic segmentation of customers at a mart by applying clustering techniques. It begins with an abstract that outlines the goal of employing advanced techniques like machine learning to target customer needs and increase sales. The introduction provides context on the increasing competitiveness of business and need for customer segmentation. The literature review summarizes several papers on topics like using machine learning for customer segmentation, comparing clustering algorithms on retail data, and dividing bank customers into clusters. The implementation section outlines the steps taken - data collection, cleaning, applying K-Means and agglomerative clustering, and exploratory data analysis. The proposed system aims to recognize the current customer situation, consolidate prior work, discover customer-attribute relationships, perform unsupervised clustering analysis and model evaluation,
Data Mining Concepts with Customer Relationship ManagementIJERA Editor
Data mining is important in creating a great experience at e-business. Data mining is the systematic way of extracting information from data. Many of the companies are developing an online internet presence to sell or promote their products and services. Most of the internet users are aware of on-line shopping concepts and techniques to own a product. The e-commerce landscape is the relation between customer relationship management (sales, marketing & support), internet and suppliers.
Big Data Analytics for Predicting Consumer BehaviourIRJET Journal
This document discusses using big data analytics and machine learning techniques to predict consumer behavior and sales trends. It begins with an introduction to consumer behavior and an overview of how analyzing customer data can provide insights. The document then discusses using data mining methods on customer data to build predictive models for tasks like sales forecasting. It proposes using a combination of random forest and linear regression algorithms on a dataset from various stores. The implementation section outlines the steps, including data preprocessing, feature extraction, applying algorithms to the data, comparing results and building the best predictive model. The goal is to determine the most accurate approach for understanding customer behavior and how they will respond in different situations.
A Survey on Customer Analytics Techniques for the Retail IndustryIRJET Journal
This document summarizes several techniques for customer analytics in the retail industry that are discussed in existing literature, including customer churn prediction, customer segmentation, and market basket analysis. It provides an overview of common algorithms used for each technique, such as classification algorithms for churn prediction, clustering algorithms for segmentation, and association rule mining for market basket analysis. It then reviews seven research papers that evaluate these techniques on retail transaction and customer data, comparing the performance of algorithms like K-means clustering, decision trees, and neural networks. The papers demonstrate how these analytical approaches can provide actionable insights for retailers to improve customer retention, target marketing, and optimize product assortments.
MBA Projects, synopsis, and synopsis of various regular as well as distance learning undergraduate and postgraduate courses for various institutions like SMU – Sikkim Manipal University, SMUDE, AIMA, AMITY, IGNOU, SCDL, JAMIA, AMU, JHU etc.
IRJET- Credit Profile of E-Commerce CustomerIRJET Journal
This document proposes using RFM (Recency, Frequency, Monetary) variables and advanced k-means clustering to create positive and negative credit profiles for e-commerce customers. This will help minimize losses by identifying genuine versus fraudulent customers. The methodology calculates credit scores based on RFM and other factors. Advanced k-means clustering is then used to segment customers into clusters like excellent, good, average, and worst. Customers in different clusters will receive different benefits or restrictions based on their predicted reliability. The goal is to reduce losses from unwanted cancellations while retaining high value customers.
Intelligent Shopping Recommender using Data MiningIRJET Journal
The document presents an intelligent shopping recommender system that uses data mining techniques. It analyzes customer purchase behavior data to provide personalized product recommendations and targeted offers. The proposed system aims to improve over traditional recommendation systems by focusing recommendations on individual customer interests and purchase histories rather than broad segments. It uses association rule mining on customer transaction data to identify patterns and predict customer tastes to provide more relevant recommendations and increased customer satisfaction compared to existing systems.
AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime ValueIIRindia
Data mining techniques are widely used in various areas of marketing management for extracting useful information.Particularly in a business-to-customer (B2C) setting, it plays an important role in customer segmentation. A retailernot only tries to improve its relationship with its customers,but also enhances its business in a manufacturer-retailer-consumer chainwith respect to this information.Although there are various approaches for customer segmentation, we have used an analytic hierarchical process based data mining technique in this regard. Customers are segmented into six clusters based on Davis-Bouldin (DB) index and K-Means algorithm.Customer lifetime value (CLV)along four dimensions, viz., Length (L), Recency(R), Frequency (F) and Monetary value (M) are considered for these clusters. Then, we apply Saaty’s analytical hierarchical process (AHP) to determine the weights of these criteria, which in turn, helps in computing the CLV value for each of the clusters and their individual rankings. This information is quite important for a retailer to design promotional strategies for improving relationship between the retailer and its customers. To demonstrate the effectiveness of this methodology, we have implemented the model, taking a real life data-base of customers of an organization in the context of an Indian retail industry.
An impact of knowledge mining on satisfaction of consumers in super bazaarsIAEME Publication
This document summarizes research on using knowledge mining techniques to study customer satisfaction levels in super bazaars. It first introduces the importance of customer satisfaction for super bazaars and defines knowledge mining. It then describes various knowledge mining techniques that can be applied, including classification, regression, time series analysis, clustering, and association rule mining. The document proposes a model for conducting customer satisfaction surveys, applying knowledge mining techniques to the data, and using the results to enhance customer satisfaction. The goal of the research is to better understand customer preferences and behaviors to improve business performance for super bazaars.
Data Mining Based Store Layout Architecture for SupermarketIRJET Journal
This document discusses using data mining techniques to develop an efficient store layout for supermarkets. It proposes using association rule mining on transaction data to uncover frequent itemsets purchased together by customers. This can help determine what products to place near each other to increase sales. The document first provides background on data mining and how it can help with decision support. It then describes how association rule mining and the Apriori algorithm can be applied to market basket analysis to analyze customer purchasing patterns and generate rules on related products. The goal is to develop a more customer-oriented store layout based on these rules rather than traditional category-based layouts.
Analysis of Sales and Distribution of an IT Industry Using Data Mining Techni...ijdmtaiir
The goal of this work is to allow a corporation to
improve its marketing, sales, and customer support operations
through a better understanding of its customers. Keep in mind,
however, that the data mining techniques and tools described
here are equally applicable in fields ranging from law
enforcement to radio astronomy, medicine, and industrial
process control. Businesses in today’s environment
increasingly focus on gaining competitive advantages.
Organizations have recognized that the effective use of data is
the key element in the next generation is to predict the sales
value and emerging trend of technology market. Data is
becoming an important resource for the companies to analyze
existing sales value with current technology trends and this
will be more useful for the companies to identify future sales
value. There a variety of data analysis and modeling techniques
to discover patterns and relationships in data that are used to
understand what your customers want and predict what they
will do. The main focus of this is to help companies to select
the right prospects on whom to focus, offer the right additional
products to company’s existing customers and identify good
customers who may be about to leave. This results in improved
revenue because of a greatly improved ability to respond to
each individual contact in the best way and reduced costs due
to properly allocated resources. Keywords: sales, customer,
technology, profit.
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Pr...IJCSEIT Journal
Association rule mining (ARM) is the process of generating rules based on the correlation between the set
of items that the customers purchase.Of late, data mining researchers have improved upon the quality of
association rule mining for business development by incorporating factors like value (utility), quantity of
items sold (weight) and profit. The rules mined without considering utility values (profit margin) will lead
to a probable loss of profitable rules.
The advantage of wealth of the customers’ needs information and rules aids the retailer in designing his
store layout[9]. An algorithm CSHURI, Customer Segmentation using HURI, is proposed, a modified
version of HURI [6], finds customers who purchase high profitable rare items and accordingly classify the
customers based on some criteria; for example, a retail business may need to identify valuable customers
who are major contributors to a company’s overall profit. For a potential customer arriving in the store,
which customer group one should belong to according to customer needs, what are the preferred functional
features or products that the customer focuses on and what kind of offers will satisfy the customer, etc.,
finds the key in targeting customers to improve sales [9], which forms the base for customer utility mining.
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
IRJET-User Profile based Behavior Identificaton using Data Mining TechniqueIRJET Journal
This document presents a model for analyzing customer behavior on online shopping sites using data mining techniques. Clickstream data is collected from customers and analyzed to predict shopping behaviors and provide recommendations. The Naive Bayes algorithm is used to classify customers into categories based on likely purchased and viewed product categories. Recommendations are then provided to customers in their predicted interested categories. The model aims to increase sales by understanding customer interests and loyalty to specific product types.
Customer Churn Prediction using Association Rule Miningijtsrd
Customer churn is one of the most important metrics for a growing business to evaluate. It is a business term used to describe the loss of clients or customers. In the retail sales and marketing company, customers have multiple choices of services and they frequently switch from one service to another. In these competitive markets, customers demand best products and services at low prices, while service providers constantly focus on getting hold of as their business goals. An increase in customer retention of just 5 can create at least a 25 increase in profit. Therefore, customer churn rate is important because it costs more to acquire new customers than it does to retain existing customers. In this paper, we apply the method to the retail sales and marketing company customer churn data set. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It will help the retail sales and marketing company to present the targeted customers with the estimated loss of clients or customers for the promotion in direct marketing. Mie Mie Aung | Thae Thae Han | Su Mon Ko "Customer Churn Prediction using Association Rule Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26818.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26818/customer-churn-prediction-using-association-rule-mining/mie-mie-aung
This document discusses research conducted by SystemicLogic on implementing product line concepts in large banks. The researchers found that while product line practices developed by SEI/CMU were relevant, a move toward "service lines" was needed to address banks' delivery of financial services through software. Key challenges identified in applying product line approaches included banks' huge invested infrastructures organized around traditional product silos, complex legacy systems, and skeptical perceptions of theoretical concepts. The researchers concluded the term "product" has different meanings, and a service line concept aligned better with banks' delivery of financial services through continually updated software processes and channels rather than discrete software products.
Proposed ranking for point of sales using data mining for telecom operatorsijdms
This study helps telecom companies in making decisions that optimize its sales points to reduce costs, also
to identify profitable customers and churn ones. This study builds two research models; physical model for
continuous mining of database where ever it resides i.e., as we have On Line Analytic Processing (OLAP)
we must have On Line Data Mining (OLDM), and logical model using Technology Acceptance Model.
Previous Studies showed that using basic information of customers, call details and customer service
related data, a model can effectively achieve accurate prediction data.
This research gives a new definition and classification for telecommunication services from the data
mining point of view. Then this research proposed a formula for total rank a shop and each term of this
formula gives a sub rank. The proposed example shows that even a shop with lower numbers of population
and visitors, it still has higher rank.
This research suggested that telecom operators has to concentrate more on their e-shopping and epayment
as it is more cost effective and use data from shops for marketing issues. Some assumptions made
in this study need to be validated using surveys, also proposed ranking should be applied on live database.
This document summarizes a research paper that proposes using an artificial neural network tuned by a simulated annealing algorithm for real-time credit card fraud detection. The paper describes how simulated annealing can be used to train the weights of a neural network model to classify credit card transactions as fraudulent or non-fraudulent based on attributes of past transactions. The algorithm is tested on a real-world credit card transaction dataset and is found to effectively classify most transactions correctly, though some misclassifications still occur.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
This document provides an overview of vertical handover decision strategies in heterogeneous wireless networks. It begins with an introduction to always best connectivity requirements in next generation networks that allow users to move between different network technologies. It then discusses the key aspects of handover management, including the three phases of initiation, decision, and execution. Various criteria for the handover decision process are described, such as received signal strength, network connection time, available bandwidth, power consumption, cost, security, and user preferences. Different types of handover decision strategies are categorized, including those based on network conditions, user preferences, multiple attributes, fuzzy logic/neural networks, and context awareness. The strategies are analyzed and their advantages/disadvantages compared.
This paper presents the design and performance comparison of a two stage
operational amplifier topology using CMOS and BiCMOS technology. This conventional op
amp circuit was designed by using RF model of BSIM3V3 in 0.6 μm CMOS technology and
0.35 μm BiCMOS technology. Both the op amp circuits were designed and simulated,
analyzed and performance parameters are compared. The performance parameters such as
gain, phase margin, CMRR, PSRR, power consumption etc achieved are compared. Finally,
we conclude the suitability of CMOS technology over BiCMOS technology for low power
RF design.
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
This paper analyzes the impact of network scalability on various physical attributes of Zigbee networks. Simulations were conducted using Qualnet to evaluate the performance of the Zigbee physical layer based on energy consumption and throughput. Energy consumption was analyzed for different modulation schemes (ASK, BPSK, OQPSK), network sizes (2-50 nodes), and clear channel assessment modes. The results showed that OQPSK and ASK had lower energy consumption than BPSK. Throughput was highest for OQPSK. While carrier sense had slightly higher throughput than other CCA modes, the energy consumption differences between CCA modes were minor.
This paper gives a brief idea of the moving objects tracking and its application.
In sport it is challenging to track and detect motion of players in video frames. Task
represents optical flow analysis to do motion detection and particle filter to track players
and taking consideration of regions with movement of players in sports video. Optical flow
vector calculation gives motion of players in video frame. This paper presents improved
Luacs Kanade algorithm explained for optical flow computation for large displacement and
more accuracy in motion estimation.
A rapid progress is seen in the field of robotics both in educational and industrial
automation sectors. The Robotics education in particular is gaining technological advances
and providing more learning opportunities. In automotive sector, there is a necessity and
demand to automate daily human activities by robot. With such an advancement and
demand for robotics, the realization of a popular computer game will help students to learn
and acquire skills in the field of robotics. The computer game such as Pacman offers
challenges on both software and hardware fronts. In software, it provides challenges in
developing algorithms for a robot to escape from the pool of attacking robots and to develop
algorithms for multiple ghost robots to attack the Pacman. On the hardware front, it
provides a challenge to integrate various systems to realize the game. This project aims to
demonstrate the pacman game in real world as well as in simulation. For simulation
purpose Player/Stage is used to develop single-client and multi-client architectures. The
multi- client architecture in player/stage uses one global simulation proxy to which all the
robot models are connected. This reduces the overhead to manage multiple robots proxy.
The single-client architecture enables only two robot models to connect to the simulation
proxy. Multi-client approach offers flexibility to add sensors to each port which will be used
distinctly by the client attached to the respective robot. The robots are named as Pacman
and Ghosts, which try to escape and attack respectively. Use of Network Camera has been
done to detect the global positions of the robots and data is shared through inter-process
communication.
In Content-Based Image Retrieval (CBIR) systems, the visual contents of the
images in the database are took out and represented by multi-dimensional characteristic
vectors. A well known CBIR system that retrieves images by unsupervised method known
as cluster based image retrieval system. For enhancing the performance and retrieval rate
of CBIR system, we fuse the visual contents of an image. Recently, we developed two
cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this
paper, we analyzed the performance of the two recommended CBIR systems at different
levels of precision using images of varying sizes and resolutions. We also compared the
performance of the recommended systems with that of the other two existing CBIR systems
namely UFM and CLUE. Experimentally, we find that the recommended systems
outperform the other two existing systems and one recommended system also comparatively
performed better in every resolution of image.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
A Proxy signature scheme enables a proxy signer to sign a message on behalf of
the original signer. In this paper, we propose ECDLP based solution for chen et. al [1]
scheme. We describe efficient and secure Proxy multi signature scheme that satisfy all the
proxy requirements and require only elliptic curve multiplication and elliptic curve addition
which needs less computation overhead compared to modular exponentiations also our
scheme is withstand against original signer forgery and public key substitution attack.
This document proposes a digital watermarking technique using LSB replacement with secret key insertion for enhanced data security. The technique works by inserting a watermark into the least significant bits of pixels in an image. A secret key is also inserted during transmission for additional security. The watermarked image is generated without noticeably impacting image quality. The proposed method was tested on sample images and successfully embedded watermarks while maintaining visual quality. The technique aims to provide copyright protection and authentication of digital images and documents.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
This paper presents a survey of Dependency Analysis of Service Oriented
Architecture (SOA) based systems. SOA presents newer aspects of dependency analysis due
to its different architectural style and programming paradigm. This paper surveys the
previous work taken on dependency analysis of service oriented systems. This study shows
the strengths and weaknesses of current approaches and tools available for dependency
analysis task in context of SOA. The main motivation of this work is to summarize the
recent approaches in this field of research, identify major issue and challenges in
dependency analysis of SOA based systems and motivate further research on this topic.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
The article presents a simple algorithm to construct minimum spanning tree and
to find shortest path between pair of vertices in a graph. Our illustration includes the proof
of termination. The complexity analysis and simulation results have also been included.
Wimax technology has reshaped the framework of broadband wireless internet
service. It provides the internet service to unconnected or detached areas such as east South
Africa, rural areas of America and Asia region. Full duplex helpers employed with one of
the relay stations selection and indexing method that is Randomized Distributed Space Time
are used to expand the coverage area of primary Wimax station. The basic problem was
identified at cell edge due to weather conditions (rain, fog), insertion of destruction because
of multiple paths in the same communication channel and due to interference created by
other users in that communication. It is impractical task for the receiver station to decode
the transmitted signal successfully at the cell edges, which increases the high packet loss and
retransmissions. But Wimax is a outstanding technology which is used for improving the
quality of internet service and also it offers various services like Voice over Internet
Protocol, Video conferencing and Multimedia broadcast etc where a little delay in packet
transmission can cause a big loss in the communication. Even setup and initialization of
another Wimax station nearer to each other is not a good alternate, where any mobile
station can easily handover to another base station if it gets a strong signal from other one.
But in rural areas, for few numbers of customers, installation of base station nearer to each
other is costlier task. In this review article, we present a scheme using R-DSTC technique to
choose and select helpers (relay nodes) randomly to expand the coverage area and help to
mobile station as a helper to provide secure communication with base station. In this work,
we use full duplex helpers for better utilization of bandwidth.
Radio Frequency identification (RFID) technology has become emerging
technique for tracking and items identification. Depend upon the function; various RFID
technologies could be used. Drawback of passive RFID technology, associated to the range
of reading tags and assurance in difficult environmental condition, puts boundaries on
performance in the real life situation [1]. To improve the range of reading tags and
assurance, we consider implementing active backscattering tag technology. For making
mobiles of multiple radio standards in 4G network; the Software Defined Radio (SDR)
technology is used. Restrictions in Existing RFID technologies and SDR technology, can be
eliminated by the development and implementation of the Software Defined Radio (SDR)
active backscattering tag compatible with the EPC global UHF Class 1 Generation 2 (Gen2)
RFID standard. Such technology can be used for many of applications and services.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
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included in the pattern have low profits, the pattern which has not taken place recently and the pattern not
actively appeared for certain span of duration. To address these issues, we incorporate the subjective
parameters like loyalty and profitability along with objective parameters like support and confidence in SPM.
II. LITERATURE STUDY
Massive amounts of business data are being generated and stored every day in database. Mining association
rules [23] from transactional data was popular and important knowledge discovery technique [20] in
90s.Association rules (ARs) of retail data can provide valuable information on customer buying behaviour.
But there are some applications which need to trap time related phenomenon called timestamp based
sequential data. The methodology was first introduced by Agrawal and Srikant , in which, Consider a dataset
consisting of ‘‘data-sequences”, which are lists of items purchased by individual customers over time. The
goal of SPM is to find all the frequent sub sequences in the dataset [2].
A. Sequential Pattern Mining (SPM)Technique
Existing SPM techniques are divided into two categories: (1)Apriori based SPM and (2) FP-growth based
SPM.[1][13]
(1) Apriori based SPM
The GSP algorithm is an extension of the A-priori model worked on a breadth-first principle which uses
“Generating-Pruning” method [25].SPADE needs only three database scans in order to extract the sequential
patterns. The main idea in this method is a clustering of the frequent sequences based on their common
prefixes and the record of the candidate sequences, loaded in main memory[27]. SPAM proposed a vertical
bitmap representation of the database for both candidate representation and support counting, which represent
the database in the main memory [3].
(2) FP-growth based SPM
FreeSpan algorithm considering the pattern-projection method for mining sequential patterns. It is the
original approach for mining sequential patterns recursively projecting the data sequences into smaller
databases.[12] This work has been continued with PrefixSpan [22]. The projected databases contain suffixes
of the data-sequences from the original database, grouped by prefixes.
B. Limitation with existing approach and variation in RFM Model
Above mentioned methods are worked on purely frequency. But the rare and high valued items which are
indeed important are neglected. Concept of recency, frequency, and monetary (RFM) introduced by Bult and
Wansbeek in SPM.[ 6] and has proven very effective [4] when applied to marketing databases. Apriori based
efficient algorithm for finding RFM based sequential patterns from customers’ data-sequences [26].Several
researchers have considered RFM variables in developing prediction and classification models. A Bayesian
Networks approach has been proposed, using RFM variables to predict a customer’s response to direct
marketing [10].Data-mining models for predicting customer loyalty [8] and customer lifetime value [11] has
been developed. Many data-mining applications have been developed to discover useful customer and market
information from the data, such as product recommendation [17], e-retailing [9], customer profiling [14][19].
To the best of our knowledge, however, this paper is the first in applying the subjective measures in SPM. As
discussed in the Introduction, to identify group of emerging customers could be very important for retailer,
which motivates this research.
C. Motivation of research
The main aim of any business: “To achieve maximum profit, every businessman is interested to identify such
crowd of customer who helps to accomplish this basic requirement.” This fundamental but strong reality
motivates us to develop such approach which fully or at least partially helps businessman to discover such a
potential buying pattern and group of potential customer to run forward their business.
III. NOVEL PROPOSAL
Problem of Conventional SPM approaches in Business environment is investigated in section 2. As discussed
in Section 1, the patterns we want to discover in Business environment are not only Recent, Frequent and
high valued. But, we are in search of such buying patterns which are really useful in business. And fulfil the
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fundamental aim of businessman, discussed in section 2. According to fundament aim of any business,
pattern should be profit achiever.
A. Limitation of existing model and features of our proposal
Most of the existing SPM model like GSP, SPAM, SPADE, Freespan, PrefixSpan works on purely
frequency, which is suffering from rare item problem. Proposed approach worked on other objective
measures along with frequency and constraints.
Above problem is resolve by RFM model [26]. To extract those items which having more monitory
value are not sufficient because monitory value gives you total sales. But how much one is gaining from
total sales is more significant which is known as profit. Consideration of profit is always more
meaningful than Sales or Monitory, which is first time introduced in our approach.
Almost all the existing methods are concentrate on the current scenario but there are some patterns which
having potential to become strong in future, which are suffering from slightly less support. Minor change
in support value can put such pattern in attention, which can be potential buying pattern for tomorrow.
Such kinds of Emerging Patterns (EPs) are well focused in our proposed approach.
Customer segmentation is taken place on bases of RFM parameters, in most of the existing work.
According to our knowledge nobody has identified potential customer group, which can be easily
identified by proposed approach.
According to our knowledge extraction of sequential patterns work on objective measures like support
and confidence. Little work has been done with consideration of other measures like lift, correlation,
conviction, leverage etc in SPM. As per our survey, SPM with subjective measure is almost untouched
area, which really needs to explore. Proposed approach focused on subjective measures like profitability,
loyalty, simplicity etc.
In most of the research customer who are recent, frequent and having high monitory value are considered
as loyal customer. But loyalty of customer not indeed depend on high RFM. Because in customer
relationship management (CRM), long term customer is more important than the customer who has
started recently to purchase from shop. Along with recency active trade during certain duration of span
which is known as compactness is equally important. Some customer are buying only household items
and moving somewhere else for high budget items like electronics items, jewellery, cloths, grocery etc.
So, it is also necessary to keep track of customer’s buying basket, which should fill with diverse items is
also important. Our proposed approach
B. Formal definition
Base Algorithm: FP growth based Prefixspan can be chosen as base algorithm for modification. Because
Theoretical (section 2) and simulation study (section 4) reveal that FP Growth based PrefixSpan outperform
Apriori based GSP[25], SPAM[3], SPADE[27] algorithm [15][22][21].
Representation of Data Sequence:Data-sequence A is represented as <(A1 a1(qty1), t1, m_sold1,m_pur1),
(A2 a2(qty2), t2, m_sold2,m_pur2), …,(An an(qtyn), tn, m_soldn,m_purn)>, where (Aj aj(qtyj), tj,
m_soldj,m_purj) means that item aj is purchased at time tj with m_purj money and in qtyj quantity which
having original value m_soldj and its having of type Aj , 1 j n, and tj-1 j for 2 j n. In the data-
sequence, if items occur at the same time, they are ordered alphabetically.
Profitable Pattern: It is important for any business to understand which patterns are profitable in terms of
money. profit is indirectly derived from Monitory constraint with some changes. Profit is depends upon two
valuable parameters: purchase price and sold price.
The Profitable constraint define item in a sequence must be more than the defined threshold value. The
Profitable constraint is formally represented as following:
CProfit - (1)
{
A sequence SS=< (q1(qty1) , t1,M_Sold1, M_Pur1 ), (q2(qty2) , M_Sold2, M_Pur2 ),...., (qm(qtym) ,M_Sold m,
M_Pur m ) > is said to be a subsequence of S only if, (1) itemset SS is a subsequence of S , SS S and (2) the
number of items in S should satisfied
( _ _ )
TProfit (2)
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Loyalty: Along with Recency, frequency and Monitory (RFM), pattern should fit in compactness criterion.A
constraint C for sequential pattern mining is a boolean function C on the set of all sequences. The problem
of constraint-based sequential pattern mining is to find the complete set of sequential patterns satisfying a
given constraint C. Constraints are design as follows[16]:
Total Monitory: The Total Monitory (TM) constraint define item in a sequence must be more than the
defined threshold value. The Total Monitory constraint is formally represented as following:
CTM (3)
{
A sequence SS=< (q1(qty1) , t1,M_Sold1, M_Pur1 ), (q2(qty2) , M_Sold2, M_Pur2 ),...., (qm(qtym) ,M_Sold m,
M_Pur m ) > is said to be a subsequence of S only if, (1) itemset SS is a subsequence of S , SS S and (2) the
number of items in S should satisfied
( _ )
TM (4)
Compactness: it derived from duration constraint. The time-stamp difference between the first and the last
transactions in a sequential pattern must be longer or shorter than a given period. Formally, a duration
constraint is in the form of
CComp (5)
where , and t is a given integer. A sequence satisfies the constraint if and only if
SDB| 1<···<ilen( ) ) s.t. 1],..., len( ) len( )] .time - 1]
Recency : sequential patterns in the sequence database must have the property such that the last timestamp of
sequence must be longer or shorter than given recency count.
Formally , Recency constraint is in the form of
C recency (6)
where , and t is a given integer. A sequence s SDB|
i1<···<ilen( ) ) s.t. 1],..., len( ) len( )
C. Evaluation of association rules
Limitation of existing SPM algorithm w.r.t objective measures: SPM algorithms use support and confidence
thresholds as objective parameters which lead to produce a huge number of rules which may not be really
interesting to user.
Generated rules are valid if they satisfy some evaluation measures. Evaluation process is needed to handle a
measure in order to evaluate its interestingness. In our approach, we propose to evaluate interestingness of
mined rules and to express the relevance of rules with following measures.[5][24][18] where, itemsets A, B
and rule X: A B as follows (refer table 1):
TABLE I: COMPREHENSIVE STUDY OF MEASURES
Measure Mathematical Formula Working Understanding
Lift
( ) =
P(A B)
P(A) P(B)
Represent probability of having B when A
occurs.
High value: stronger associations
Low value: weak associations.
Loevinger Loevinger(X) =
1
P(A) P( B)
P(A B)
It normalizes the centred confidence of a
rule according to the probability of not
satisfying its consequent part B.
High value: stronger associations
Low value: weak associations.
Conviction ( )
=
1 supp(B)
1 conf(A B)
It is interpreted as the ratio of the expected
frequency that A occurs without B
(Incorrect prediction).
It attempts to measure the degree of
implication of a rule.
leverage leverage(A -> B) = P(A
and B) - (P(A)P(B))
It is a measure in which number of counting
is obtained from the co-occurrence of the
antecedent and consequent of the rule from
the expected value.
it find out how many more units
(items A and B together) are sold than
expected from the independent sells.
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IV. SIMULATION STUDY
We have performed a simulation study on secondary real-time dataset. SPM Algorithms were implemented
in Java and tested on an Intel Core Duo Processor with 2GB main memory under Windows XP operating
system.
A. Simulation study of existing SPM techniques
We have performed a simulation study to compare the performances of the algorithms: GSP,SPADE,SPAM
and PrefixSpan, Comparison is based on runtime, frequent sequence patterns, memory utilization on various
(10 % to 60%.) support threshold. We have performed following experiment on JAVA based SPMF
framework (Sequential Pattern Mining Framework) designed by Philippe (Sequential Pattern Mining
Framework : http://www.philippe-fournier-viger.com/spmf ) on real time dataset mashroom.
On comparing various algorithms of sequential pattern mining algorithm. The following points can be
observed from above simulation:
Approx 49% and 24% more execution time is taken by GSP and SPADE w.r.t. prefixSpan. SPAM is
consuming 18% less execution time to generate sequential patterns.(refer fig1)
Almost same frequent sequences are generated for 50% and above support count. Same sequences are
generated with SPAM and PrefixSpan in all the cases. 10% and 11% more sequences are generated by
GSP and SPADE respectively. (refer fig 2)
Comparatively less memory is occupied by GSP and SPADE w.r.t. PrefixSpan.11% less memory is
occupied by SPAM w.r.t. PrefixSpan. (refer fig 3)
Fig.1: memory Vs support Fig.2: no of pattern vs. support
Fig.3: execution time vs. Support
B. Rules generation for various Measures
Performance of various measures based on results obtained using WEKA on real time dataset contact lanse.
Here we have arranged the rules respect to various measures. Also we have observed the values of other
measures for the each rule.(refer fig 4,5,6)
Performance of various measures based on results obtained using WEKA on real life dataset super market for
FP-Growth method (refer table 2 and fig. 7):
Following observation can be made for above experiments:
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Leverage is highly associated with confidence value. For top five rules in list of lift having high
confidence value(1). Conviction values are also almost in decreasing order.
The rule ’ tear-prod-rate=reduced ==> contact-lenses=none’ having conviction value 4.5 is on top for
leverage major and conviction major table. Also its having confidence value 1 which is highest in list of
confidence.
Fig 4: Top 10 rules respect to conviction Fig 5: Top 10 rules respect to lift
Fig 6: Top 10 rules respect to leverage
Only 0.25% of rules are generated as compared to lift and conviction. Lift and conviction measures are
giving vast range of rules. So decision maker can observe all possible association which is also useful in
some application. Confidence measure is giving precise range of rules. It emphasise on strong rule.
TABLE II : NUMBER OF RULES GENERATED FOR VARIOUS MEASURES
Measure Generated Rules
Lift 181292
Confidence 455
Conviction 181291
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C. Simulation study of Emerging Patterns for various threshold values
There are some patterns which are not strong currently because of its slightly high support value, which
having potential to become strong by changing its support values and the pattern which are lies on boundary
can be selected. Here we have done experiment by changing threshold by 1% and 2%.(refer fig 8,fig 9,fig10)
Fig 7: Number of rules generated for various measures (ref table 2)
Fig 8: execution time for various support values fig 9: frequent sequence for various support
Fig 10: memory occupied vs. support
Changing boundary threshold by 1% and 2% in support threshold of 30% is finding 13% and 27% more
patterns which are potential but not yet discovered. Same way 23% and 12% more potential patterns are
investigate for 20% support. Discovering more patterns is taking more execution time by 13.5% and 17% for
1% and 2% less boundary value respectively. Memory consumption is almost same by difference of 0.1%-
1%.
D. Simulation for time span window
Here we have done experiment, how specific duration span is giving user specified interesting pattern. We
have done experiment on conventional Apriori method and time window based Apriori method.
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More no of sequential rules are generated by conventional Apriori based method w.r.t. time window based
Apriori algorithm (refer fig.11).Less sequential patterns are generated by reducing time window size from 4
to 2 (refer fig.12). More execution time is taken by conventional PrefixSpan w.r.t time window based (refer
fig.13).
Fig 11: support vs. Sequential pattern fig 12: support vs. Execution time (ms)
Fig 13: support vs. Memory (mb)
V. CONCLUSION
Comparatively less work has been done in area of emerging customer. Most of the researchers have focused
either on frequency alone or Recency, Frequency and Monitory (RFM) as an evaluation parameters for SPM
and customer evolution which are not sufficient; here we have evaluated more vital parameters which are
essential for classification of customer. In our approach identification of new generation customers taken
place based on subjective measures like profitability and loyalty with SPM. Technique recognizes next
generation customer with the help of PrefixSpan based Emerging Patterns (EPs) in sequential Mining.
REFERENCE
[1] R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 1994 Int’l Conf. Very Large
Data Bases (VLDB ’94), pp. 487-499, Sept. 1994
[2] Agrawal R. And Srikant R. ‘Mining Sequential Patterns.’, In Proc. of the 11th Int'l Conference on Data Engineering,
Taipei, Taiwan, March 1995
[3] AYRES, J., FLANNICK, J., GEHRKE, J., AND YIU, T., ‘Sequential pattern mining using a bitmap representation’,
In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining-
2002.
[4] Blattberg et al., 2008 Blattberg, R.C.; Kim, B-D. & Neslin, S.A. (2008). Database Marketing: Analyzing and
Managing Customers, Chapter 12, pp. 323-337, Springer, ISBN: 978-0387725789, New York,USA.
[5] Brijs, T., Vanhoof, K. and Wets, G. (2003), ‘Defining interestingness for association rules’, International Journal of
Information Theories and Applications 10(4), 370–376.
[6] Bult, J. R., and Wansbeek, T. J. Optimal selection for direct mail. Marketing Science, 14, 4, 1995, 378–394.
[7] C K Bhensdadia, Y P Kosta,’ An Efficient Algorithm for Mining Frequent Sequential Patterns and Emerging
Patterns with Various Constraints’, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-
2307, Volume-1, Issue-6, January 2012
9. 516
[8] Cheng, C. H., and Chen, Y. S. Classifying the segmentation of customer value via RFM model and RS theory.
Expert Systems with Applications, 36, 3, 2009, 4176–4184.Cheng and Chen 2009
[9] Chen, Y. L., Tang, K., Shen, R. J., and Hu, Y. H. Market basket analysis in a multiplestore environment. Decision
Support Systems, 40, 2, 2005, 339–354.
[10] Cui, G., Wong, M. L., and Lui, H. K. Machine learning for direct marketing response models: Bayesian networks
with evolutionary programming. Management Science, 52, 4, 2006, 597–612.Cui et al. 2006
[11] Etzion, O., Fisher, A., and Wasserkrug, S. e-CLV: A modeling approach for customer lifetime evaluation in e-
Commerce domains, with an application and case study for online auction. Information Systems Frontiers, 7, 4–5,
2005, 421–434.Etzion et al. 2005
[12] Han J., Dong G., Mortazavi-Asl B., Chen Q., Dayal U., Hsu M.-C.,’ Freespan: Frequent pattern-projected sequential
pattern mining’, Proceedings 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD’00), 2000, pp. 355-359.
[13] J. Han, J. Pei, and Y. Yin, ‘Mining Frequent Patterns without Candidate Generation’,Proc. 2000 ACM-SIGMOD
Int’l Conf. Management of Data (SIGMOD ’00), pp. 1-12, May 2000.
[14] Hu, H. L., and Chen, Y. L. Mining typical patterns from databases. Information Sciences, 178, 19, 2008, 3683–3696.
[15] Irfan Khan, Anoop Jain,’ Comprehensive Survey on Sequential Pattern Mining’, International Journal of
Engineering Research & Technology (IJERT) Vol. 1 Issue 4, June – 2012
[16] Jian Pei, Jiawei Han, Wei Wang, “Constraint-based sequential pattern mining : the pattern growth methods”, J Intell
Inf Syst , Vol. 28, No.2, pp. 133 –160 , 2007
[17] Lawrence, R. D., Almasi, G. S., Kotlyar, V., Viveros, M. S., and Duri, S. S. Personalization of supermarket product
recommendations. Data Mining and Knowledge Discovery, 5, 1–2, 2001, 11–32.
[18] LIQIANG GENG AND HOWARD J. HAMILTON,’Interestingness Measures for Data Mining: A Survey’ ACM
Computing Surveys, Vol. 38, No. 3, Article 9, Publication date: September 2006.
[19] Mahdavi, I., Cho, N., Shirazi, B., and Sahebjamnia, N. Designing evolving user profile in e-CRM with dynamic
clustering of Web documents. Data and Knowledge Engineering, 65, 2, 2008, 355–372.
[20] Ming-Syan Chen, Jiawei Han, and Philip S. Yu. Data mining: An overview from a database perspective. IEEE
Transactions on Knowledge and Data Engineering, 8(6):866–883, December 1996.
[21] Desai Niti , Dr.Amit Ganatra, ’Sequential Pattern Mining Methods: A Snap Shot’, IOSR Journal of Computer
Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 10, Issue 4 (Mar. - Apr. 2013), PP 12-20
[22] J. Pei, J. Han, B. Mortazavi-Asi, H. Pino, ‘PrefixSpan: Mining Sequential Patterns Efficiently by Prefix- Projected
Pattern Growth’, ICDE'01, 2001.
[23] Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large
databases. In SIGMOD-93, pages 207–216, May 1993.
[24] Ramaswamy, S., Mahajan, S. and Silberschatz, A. (1998), On the discovery of interesting patterns in association
rules, in ‘Proceedings of the 24rd International Conference on Very Large Data Bases’, Morgan Kaufmann
Publishers Inc., pp. 368–379.
[25] Srikant R. and Agrawal R.,’Mining sequential patterns: Generalizations and performance improvements’,
Proceedings of the 5th International Conference Extending Database Technology, 1996, 1057, 3-17.
[26] Yen-Liang Chen , Mi-Hao Kuo , Shin-Yi Wu, Kwei Tang , ‘Discovering recency, frequency, and monetary (RFM)
sequential patterns from customers’ purchasing data’, Electronic Commerce Research and Applications 8 (2009)
241–251
[27] M. Zaki, ‘SPADE: An Efficient Algorithm for Mining Frequent Sequences’, Machine Learning, vol. 40, pp. 31-60,
2001.