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.
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.
Consumer analytics is the process businesses adopt to capture and analyze customer data to make better business decisions via predictive analytics. It is a method of turning data into deep insights to predict customer behavior. It may also be regarded as the process by which data can be turned into predictive insights to develop new products, new ways to package existing products, acquire new customers, retain old customers, and enhance customer loyalty. It helps businesses break big problems into manageable answers. This paper is a primer on consumer analytics. Matthew N. O. Sadiku | Sunday S. Adekunte | Sarhan M. Musa "Consumer Analytics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33511.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/33511/consumer-analytics-a-primer/matthew-n-o-sadiku
A Novel Intelligence-based e-Procurement System to offer Maximum Fairness Ind...IJECEIAES
A perfect auction policy is one of the most strategic elements that contribute to success factor for any e-Procurement system. An auction policy can be only term as an effective if it really offer win-win situation to both the bidder as well as to the merchant. After reviewing existing studies on e-Procurement system, it is found that there isno effective research work focusing on this point and maximum research contribution has limited its scope to certain application or case studis. Hence, the proposed system introduces a novel eProcurement system which is equipped by an itelligence-building process for performing predictive analysis of ongoing auction process. A mathematical modelling is implemented where all teh variables have been formed using practical implementation of auction system and followed by optimization process using regression-based approach. The study outcome shows that proposed system offers better response time and higher predictive accuracy in contrast to existing approaches.
PREDICTIVE BUSINESS INTELLIGENCE: CONSUMER GOODS SALES FORECASTING USING ARTI...IAEME Publication
Business competition between manufacturing businesses in Indonesia is getting
tighter along with the development of businesses from competing companies that have
similar businesses. One strategy that can be applied by this company is Business
Intelligence, that is by utilizing the data that is already available to help in better
decision making, such as decisions based on facts stored in the data, precisely namely
the lack of errors in the presentation of reports, and fast that is, cut down on the time
for making the usual report. The method proposed by the author is a method that can
be used to predict sales value based on existing sales data (sales forecasting). By
implementing Business Intelligence and data mining, companies can learn from the
data that has been collected, can evaluate the performance of the sales department,
can understand market trends from the products sold, and can predict future sales
levels. In addition, Business Intelligence can display detailed transaction data
recapitulation quickly.
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.
Consumer analytics is the process businesses adopt to capture and analyze customer data to make better business decisions via predictive analytics. It is a method of turning data into deep insights to predict customer behavior. It may also be regarded as the process by which data can be turned into predictive insights to develop new products, new ways to package existing products, acquire new customers, retain old customers, and enhance customer loyalty. It helps businesses break big problems into manageable answers. This paper is a primer on consumer analytics. Matthew N. O. Sadiku | Sunday S. Adekunte | Sarhan M. Musa "Consumer Analytics: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33511.pdf Paper Url: https://www.ijtsrd.com/other-scientific-research-area/other/33511/consumer-analytics-a-primer/matthew-n-o-sadiku
A Novel Intelligence-based e-Procurement System to offer Maximum Fairness Ind...IJECEIAES
A perfect auction policy is one of the most strategic elements that contribute to success factor for any e-Procurement system. An auction policy can be only term as an effective if it really offer win-win situation to both the bidder as well as to the merchant. After reviewing existing studies on e-Procurement system, it is found that there isno effective research work focusing on this point and maximum research contribution has limited its scope to certain application or case studis. Hence, the proposed system introduces a novel eProcurement system which is equipped by an itelligence-building process for performing predictive analysis of ongoing auction process. A mathematical modelling is implemented where all teh variables have been formed using practical implementation of auction system and followed by optimization process using regression-based approach. The study outcome shows that proposed system offers better response time and higher predictive accuracy in contrast to existing approaches.
PREDICTIVE BUSINESS INTELLIGENCE: CONSUMER GOODS SALES FORECASTING USING ARTI...IAEME Publication
Business competition between manufacturing businesses in Indonesia is getting
tighter along with the development of businesses from competing companies that have
similar businesses. One strategy that can be applied by this company is Business
Intelligence, that is by utilizing the data that is already available to help in better
decision making, such as decisions based on facts stored in the data, precisely namely
the lack of errors in the presentation of reports, and fast that is, cut down on the time
for making the usual report. The method proposed by the author is a method that can
be used to predict sales value based on existing sales data (sales forecasting). By
implementing Business Intelligence and data mining, companies can learn from the
data that has been collected, can evaluate the performance of the sales department,
can understand market trends from the products sold, and can predict future sales
levels. In addition, Business Intelligence can display detailed transaction data
recapitulation quickly.
A widely used approach for gaining insight into the heterogeneity of consumer’s buying behavior is market segmentation. Conventional market segmentation models often ignore the fact that consumers’ behavior may evolve over time. Therefore retailers consume limited resources attempting to service unprofitable consumers. This study looks into the integration between enhanced Recency, Frequency, Monetary (RFM) scores and Consumer Lifetime Value (CLV) matrix for a medium size retailer in the State of Kuwait. A modified regression algorithm investigates the consumer purchase trend gaining knowledge from a pointof-sales data warehouse. In addition, this study applies enhanced normal distribution formula to remove outliers, followed by soft clustering Fuzzy C-Means and hard clustering Expectation Maximization (EM) algorithms to the analysis of consumer buying behavior. Using cluster quality assessment shows EM algorithm scales much better than Fuzzy C-Means algorithm with its ability to assign good initial points in the smaller dataset.
Market basket analysis | Association Rules Mining | R ProgrammingNavjyotsinh Jadeja
In this presentation, we have discussed Market Basket Analysis and explained how to find Frequent Item set using Association Rule Mining.
Also, we have discussed concepts of SUPPORT, CONFIDENCE and also, seen an example for the same.
This will be helpful for GTU IOT subject course understanding too!
If you like please subscribe to our channel and turn notifications on for future videos.
Follow us on:
Website: http://www.edtechnology.in/
Instagram: https://www.instagram.com/ed.tech/
Facebook: https://www.facebook.com/Edtech18/
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
Mining the Web Data for Classifying and Predicting Users’ RequestsIJECEIAES
Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client‟s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands.
Transaction Profitability Using HURI Algorithm [TPHURI]ijbiss
Business intelligence (BI) is formulation of business strategies which help organizations to achieve its objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI systems can be utilized in business decision-making such as retail shelf management, catalog design, customer segmentation, cross-selling, quality improvement and bundling products marketing.
ARM technique is used for the identification of frequent itemsets from huge databases and then generating strong association rules by considering each item having same value. But in a large number of real world applications, items have different values according to their impact on the respective decision making processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data mining researchers are continuously improving the quality of ARM technique by incorporating the utility of items. The utility of item is decided by its contribution towards the business profit or quantity of the item sold, etc. Hence Utility mining focuses on identifying the itemsets with high utilities.
Jyothi et al proposed HURI algorithm in [2] for producing high utility rare itemset according to users’ interest. An algorithm Transaction Profitability using HURI [TPHURI] is proposed in this paper which is a modified version of HURI. TPHURI finds profitable transactions consisting of high utility rare items and also finds the share of such items in the overall profit of the transactions.
TRANSACTION PROFITABILITY USING HURI ALGORITHM [TPHURI]ijbiss
Business intelligence (BI) is formulation of business strategies which help organizations to achieve its
objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of
the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI
systems can be utilized in business decision-making such as retail shelf management, catalog design,
customer segmentation, cross-selling, quality improvement and bundling products marketing.
ARM technique is used for the identification of frequent itemsets from huge databases and then generating
strong association rules by considering each item having same value. But in a large number of real world
applications, items have different values according to their impact on the respective decision making
processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data
mining researchers are continuously improving the quality of ARM technique by incorporating the utility of
items. The utility of item is decided by its contribution towards the business profit or quantity of the item
sold, etc. Hence Utility mining focuses on identifying the itemsets with high utilities.
Jyothi et al proposed HURI algorithm in [2] for producing high utility rare itemset according to users’
interest. An algorithm Transaction Profitability using HURI [TPHURI] is proposed in this paper which is
a modified version of HURI. TPHURI finds profitable transactions consisting of high utility rare items and
also finds the share of such items in the overall profit of the transactions.
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.
Analysis of the Awareness Level of Customers about the different Retailing Te...AI Publications
The increased digitization of the world has created a new kind of shopper. Consumers are demanding more and more from their in-store experience, forcing companies to innovate quickly. The main aim of the study is Analysis of The Awareness Level of Customers About the Different Retailing Technologies. We will design a self-administered survey using multi-item constructs for measuring the phenomena of interest. We will utilize appropriate scales, such as those measuring levels of agreement, significance, and satisfaction. According to the results of this study, consumers have a broad knowledge of technological advancements but a narrower understanding of vending machines.
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.
Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion...IJECEIAES
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.
A widely used approach for gaining insight into the heterogeneity of consumer’s buying behavior is market segmentation. Conventional market segmentation models often ignore the fact that consumers’ behavior may evolve over time. Therefore retailers consume limited resources attempting to service unprofitable consumers. This study looks into the integration between enhanced Recency, Frequency, Monetary (RFM) scores and Consumer Lifetime Value (CLV) matrix for a medium size retailer in the State of Kuwait. A modified regression algorithm investigates the consumer purchase trend gaining knowledge from a pointof-sales data warehouse. In addition, this study applies enhanced normal distribution formula to remove outliers, followed by soft clustering Fuzzy C-Means and hard clustering Expectation Maximization (EM) algorithms to the analysis of consumer buying behavior. Using cluster quality assessment shows EM algorithm scales much better than Fuzzy C-Means algorithm with its ability to assign good initial points in the smaller dataset.
Market basket analysis | Association Rules Mining | R ProgrammingNavjyotsinh Jadeja
In this presentation, we have discussed Market Basket Analysis and explained how to find Frequent Item set using Association Rule Mining.
Also, we have discussed concepts of SUPPORT, CONFIDENCE and also, seen an example for the same.
This will be helpful for GTU IOT subject course understanding too!
If you like please subscribe to our channel and turn notifications on for future videos.
Follow us on:
Website: http://www.edtechnology.in/
Instagram: https://www.instagram.com/ed.tech/
Facebook: https://www.facebook.com/Edtech18/
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
Mining the Web Data for Classifying and Predicting Users’ RequestsIJECEIAES
Consumers are the most important asset of any organization. The commercial activity of an organization booms with the presence of a loyal customer who is visibly content with the product and services being offered. In a dynamic market, understanding variations in client‟s behavior can help executives establish operative promotional campaigns. A good number of new consumers are frequently picked up by traders during promotions. Though, several of these engrossed consumers are one-time deal seekers, the promotions undeniably leave a positive impact on sales. It is crucial for traders to identify who can be converted to loyal consumer and then have them patronize products and services to reduce the promotion cost and increase the return on investments. This study integrates a classifier that allows prediction of the type of purchase that a customer would make, as well as the number of visits that he/she would make during a year. The proposed model also creates outlines of users and brands or items used by them. These outlines may not be useful only for this particular prediction task, but could also be used for other important tasks in e-commerce, such as client segmentation, product recommendation and client base growth for brands.
Transaction Profitability Using HURI Algorithm [TPHURI]ijbiss
Business intelligence (BI) is formulation of business strategies which help organizations to achieve its objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI systems can be utilized in business decision-making such as retail shelf management, catalog design, customer segmentation, cross-selling, quality improvement and bundling products marketing.
ARM technique is used for the identification of frequent itemsets from huge databases and then generating strong association rules by considering each item having same value. But in a large number of real world applications, items have different values according to their impact on the respective decision making processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data mining researchers are continuously improving the quality of ARM technique by incorporating the utility of items. The utility of item is decided by its contribution towards the business profit or quantity of the item sold, etc. Hence Utility mining focuses on identifying the itemsets with high utilities.
Jyothi et al proposed HURI algorithm in [2] for producing high utility rare itemset according to users’ interest. An algorithm Transaction Profitability using HURI [TPHURI] is proposed in this paper which is a modified version of HURI. TPHURI finds profitable transactions consisting of high utility rare items and also finds the share of such items in the overall profit of the transactions.
TRANSACTION PROFITABILITY USING HURI ALGORITHM [TPHURI]ijbiss
Business intelligence (BI) is formulation of business strategies which help organizations to achieve its
objectives and to predict its future. Data mining is often referred as BI in the domain of business. One of
the major tasks in data mining is Association Rule Mining (ARM). ARM techniques incorporated in BI
systems can be utilized in business decision-making such as retail shelf management, catalog design,
customer segmentation, cross-selling, quality improvement and bundling products marketing.
ARM technique is used for the identification of frequent itemsets from huge databases and then generating
strong association rules by considering each item having same value. But in a large number of real world
applications, items have different values according to their impact on the respective decision making
processes. Traditional ARM techniques cannot fulfil the arising demands from these applications. The data
mining researchers are continuously improving the quality of ARM technique by incorporating the utility of
items. The utility of item is decided by its contribution towards the business profit or quantity of the item
sold, etc. Hence Utility mining focuses on identifying the itemsets with high utilities.
Jyothi et al proposed HURI algorithm in [2] for producing high utility rare itemset according to users’
interest. An algorithm Transaction Profitability using HURI [TPHURI] is proposed in this paper which is
a modified version of HURI. TPHURI finds profitable transactions consisting of high utility rare items and
also finds the share of such items in the overall profit of the transactions.
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.
Analysis of the Awareness Level of Customers about the different Retailing Te...AI Publications
The increased digitization of the world has created a new kind of shopper. Consumers are demanding more and more from their in-store experience, forcing companies to innovate quickly. The main aim of the study is Analysis of The Awareness Level of Customers About the Different Retailing Technologies. We will design a self-administered survey using multi-item constructs for measuring the phenomena of interest. We will utilize appropriate scales, such as those measuring levels of agreement, significance, and satisfaction. According to the results of this study, consumers have a broad knowledge of technological advancements but a narrower understanding of vending machines.
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.
Framework to Analyze Customer’s Feedback in Smartphone Industry Using Opinion...IJECEIAES
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact.
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 ...
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.
Similar to CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Profitability (20)
ANALYSIS OF EXISTING TRAILERS’ CONTAINER LOCK SYSTEMS IJCSEIT Journal
Trailers carry large containers to various destinations in the world. These are manually locked on to
trailers as they move through these long distances. Security mainly refers to the safety of a state,
organization, property, and individuals against criminal activity. The study was made to analyze the
existing trailer locks and the insecurity being experienced currently. The study also focused on creating a
background to building an automated lock system for auto-mobiles. Findings showed that there are various
container types like the General Purpose containers, the Hard-Top containers and the Open-Top among
others. Similarly, the twist locks were the ones revised for this study. The study also discussed the
weaknesses of the twist locks, most especially the non-notification on unsecured locks. This causes leads to
accidents and wastage of lives and property. The study finally proposed an automated lock system to
overcome these weaknesses to some good extent.
A MODEL FOR REMOTE ACCESS AND PROTECTION OF SMARTPHONES USING SHORT MESSAGE S...IJCSEIT Journal
The smartphone usage among people is increasing rapidly. With the phenomenal growth of smartphone
use, smartphone theft is also increasing. This paper proposes a model to secure smartphones from theft as
well as provides options to access a smartphone through other smartphone or a normal mobile via Short
Message Service. This model provides option to track and secure the mobile by locking it. It also provides
facilities to receive the incoming call and sms information to the remotely connected device and enables the
remote user to control the mobile through SMS. The proposed model is validated by the prototype
implementation in Android platform. Various tests are conducted in the implementation and the results are
discussed.
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search and application resulting in good integration with information technology including the internet and
intelligent agent-based architectures, there are still many areas that need to be enhanced. Two such areas
include the quality of jobs associated with applicants in the job search by profiling the needs of employers
against the needs of prospective employees and the security and verifications schemes integrated to reduce
the instances of fraud and identity theft. The integration of mobile, intelligent agent, and cryptography
technologies provide benefits such as improved accessibility wirelessly, intelligent dynamic profiling, and
increased security. With this in mind we propose the intelligent mobile agents instead of human agents to
perform the Job search using fuzzy preferences which is been published elsewhere and application
operations incorporating the use of agents with a trust authority to establish employer trust and validate
applicant identity and accuracy. Our proposed system incorporates design methodologies to use JADELEAP
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features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector
and diagonal vectors are computed for these matrices. Global feature vector is generated for face
recognition. Experiments are performed on benchmark face YALE database. Results indicate that the
proposed method gives better recognition performance in terms of average recognized rate and retrieval
time compared to the existing methods.
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ability to require more instances of authentication in such a quick and easy manner that users are not
bothered by the additional requirements. In this paper, we have given a brief introduction about
biometrics. Then we have given the information regarding the intrusion detection system and finally we
have proposed a method which is based on fingerprint recognition which would allow us to detect more
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content are very similar from one video to the other. The main focus of this paper is to detect that the query
video is present in the video database with robustness depending on the content of video and also by fast
search of fingerprints. The Fingerprint Extraction Algorithm and Fast Search Algorithms are adopted in
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Fingerprint Extraction algorithm is employed which extracts a fingerprint through the features from the
image content of video. The images are represented as Temporally Informative Representative Images
(TIRI). Then, the second step is to find the presence of copy of a query video in a video database, in which
a close match of its fingerprint in the corresponding fingerprint database is searched using inverted-filebased
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rotation and frame drop. Thus the performance of the proposed system on an average shows high true
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user. It is observed via computer simulation that the performance of the interleaved coded based proposed
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Classifiers that takes advantage of weighted Association Rule Mining is already being proposed. However,
there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute
domains. This paper proposes a new Fuzzy Weighted Associative Classifier (FWAC) that generates
classification rules using Fuzzy Weighted Support and Confidence framework. The naïve approach can be
used to generating strong rules instead of weak irrelevant rules. where fuzzy logic is used in partitioning
the domains. The problem of Invalidation of Downward Closure property is solved and the concept of
Fuzzy Weighted Support and Fuzzy Weighted Confidence frame work for Boolean and quantitative item
with weighted setting is generalized. We propose a theoretical model to introduce new associative classifier
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represents it by a set of vectors called feature. Features like power spectrum density, frequency at
maximum power carry speaker information. The feature is extracted using First Fourier Transform (FFT)
algorithm. The task of the back-end system (also called classifier) is to create a gender model to recognize
the gender from his/her speech signal in recognition phase. This paper also presents the digital processing
of a speech signals (pronounced “A” and “B”) which are taken from 10 persons, 5 of them are Male and
the rest of them are Female. Power Spectrum Estimation of the signal is examined .The frequency at
maximum power of the English Phonemes is extracted from the estimated power spectrum. The system uses
threshold technique as identification tool. The recognition accuracy of this system is 80% on average.
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retrieved and the first automatic relevance feedback is generated. The combined similarity of textual and
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irrelevant. The feedback drives a feature re-weighting process and is routed to the particle swarm
optimizer. Instead of classical swarm update approach, the swarm is split, for each swarm to perform the
search in parallel, thereby increasing the performance of the system. It provides a powerful optimization
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goals without any human interaction - to cluster relevant images using meta-heuristics and to dynamically
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cross-layer framework -a MAC network cross-layer design for forwarder selection (or routing) and a
MAC-PHY for relay selection. Wireless networks suffers huge number of communication at the same time
leads to increase in collision and energy consumption; hence focused on new Contention access method
that uses a dynamical change of channel access probability which can reduce the number of contention
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watershed transform followed by naive Bayes classification of each region using the features, mean and
standard deviation, is presented. Also, a post processing step is added to re-classify the small chromosome
segments to the neighboring larger segment for reducing the chances of misclassification. The approach
provided improved accuracy when compared to the pixel-by-pixel approach. The approach was tested on
40 images from the dataset and achieved an accuracy of 84.21 %.
Steganography is the technique of hiding a confidential message in an ordinary message and the extraction
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used. Here steganography is done on the skin portion of an image. First skin portion of an image is
detected. Random pixels are selected from that detected region using a pseudo-random number generator.
The bits of the secret message will be embedded on the LSB of these random pixels. An analysis is done to
check the efficiency and robustness of the proposed method. The aim of this work is to show that
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which has better performance like narrower main lobe width, minimum side lobe peak compared to the
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extracted by threshold segment and morphology process, and the features of invariant moment and area
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accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images
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distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
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such flexibility of web based training system, system interactivity and system enjoyment, in order to explain
the employees’ intention to use web based training system. A total of 290 employees have participated in
this study. The findings of the study revealed that performance expectancy, facilitating conditions, social
influence and system flexibility have direct effect on the employees’ intention to use web based training
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CSHURI – Modified HURI algorithm for Customer Segmentation and Transaction Profitability
1. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
DOI : 10.5121/ijcseit.2012.2208 79
CSHURI – Modified HURI algorithm for
Customer Segmentation and Transaction
Profitability
Jyothi Pillai 1
and O.P.Vyas 2
1
Associate Professor, Bhilai Institute Of Technology, Durg, Chhattisgarh, India
jyothi_rpillai@rediffmail.com
2
Professor, Indian Institute of Information Technology Allahabad, U.P., India
dropvyas@gmail.com
ABSTRACT
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.
Keywords
Association Rule Mining, Customer Segmentation, Utility Mining, Rare itemsets
1. INTRODUCTION
Many business enterprises accumulate large quantities of data from day-to-day operations. For
example, huge amount of customer purchase data are collected daily at the checkout counters or
grocery stores. Retailers are interested in analyzing the data to learn about the purchasing
behaviour of the customers. Such valuable information can be used to support a variety of
business related applications such as marketing promotions, inventory management and customer
relation management [10].
Data mining techniques can be used to support a wide range of business intelligence applications
such as customer profiling, targeted marketing, work flow management, store layout and fraud
2. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
80
detection. It can also help retailers answer important business questions such as the most
important customers or most profitable transactions, etc. [10]
Over the years Data Mining is used to understand the consumer buying behaviour using various
techniques. Researcher selects Market Basket Analysis for his data analysis because Market
Basket analysis is a tool of knowledge discovery about co-occurrence of nominal or categorical
items. Market Basket Transaction or market Basket Analysis is a data mining technique to derive
association between data sets. The discovery of interesting association relationship among huge
amounts of customer transaction records can help in many business decision making processes
such as catalog design, cross marketing and loss-leader analysis[2].
The association analysis described so far is based on the pretext that the presence of an item in a
transaction is more important than its absence. As a consequence patterns that are rarely found in
a database are often considered to be uninteresting and are eliminated using the support measure.
Such patterns are known as infrequent patterns. An infrequent pattern is an itemset or a rule
whose support is less than the minimum support threshold [10]. In most business applications,
frequent itemsets may not generate much profit. Rare itemsets are very important and can be
further promoted together because they possess high associations and can bring some acceptable
profits. For major expansion drive, the stores can shortlist high profitable items and can increase
its quantity and thereby can earn profit.
Point-of-sale data collection (bar code scanners, radio-frequency identification and smartcard
technology) have allowed retailers to collect up-to-the minute data about customer purchases at
the checkout counters of their stores. Retailers can utilize this information, along with other
business critical data such as Weblogs from e-commerce web sites and customer service records
from call centres, to help them better understand the needs of their customers and make more
informed business decisions [10].
It is proposed that Customer Utility Mining, using the CSHURI algorithm (Customer
Segmentation using HURI), finds customers who purchase high profitable rare items and
accordingly classify customers for providing good customers’ service. After finding such
customers, they can be categorized and accordingly gold or silver cards can be issued for these
types of customers or a new scheme can be introduced for them. These customers provide
maximum profit to the overall transaction scenario. Stores can use its customer base as a
bargaining power to strike discount deals or can gain wholesale trust of consumers in a very short
span. The outcome of CSHURI would enable the top management or business analyst in crucial
decision-making such as catalog design, providing credit facility, cross marketing, finalizing
discount policy, analyzing consumers’ buying behaviour, organizing shelf space, loss-leader
analysis and quality improvement in supermarket.
In this paper, it is proposed that CSHURI can be used both for Customer Utility Mining and
Transaction Utility Mining for classifying customers and for finding profitable transactions
containing rare itemsets. The rest of paper is organized as follows. Section 2 presents some
related works. Section 3 and section 4 discuss theoretical definitions and proposed CSHURI
algorithm; the modified version of HURI algorithm Section 5 presents conclusion and future
work.
2. LITERATURE SURVEY
The problem of utility-based itemset mining is to discover the itemsets that are significant
according to their utility values. In [13], Yao et al propose a utility-based itemset mining
3. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
81
approach which permits users to quantify their preferences concerning the usefulness of itemsets
using utility values. Two algorithms UMining and UMining_H were proposed, for utility-based
itemset mining by incorporating pruning strategies. UMining guarantees that all high utility
itemsets are found, while the heuristic share based methods, UMining_H, may miss many
relevant itemsets.
David et al presented a new algorithm, MINIT ( MINimal Infrequent iTemsets), for finding
minimal infrequent or minimal occurrent itemsets. Initially, a ranking of items is prepared by
computing the support of each of the items and then creating a list of items in ascending order of
support. Minimal infrequent itemsets are discovered by considering each item ij in rank order,
recursively calling MINIT on the support set of the dataset with respect to ij considering only
those items with higher rank than ij , and then checking each candidate MII against the original
dataset[11].
In the Frequent Itemset Mining problem, the occurrence of each item in a transaction is
represented by a binary value without considering its quantity or an associated weight such as
price or profit. However, quantity and weight are significant for addressing real world decision
problems that require maximizing the utility in an organization. For example, selling a laser
printer may occur less frequently than sale of printer ink in an electronic superstore, but the
former gives a much higher profit per unit sold. The high utility itemset mining problem is to find
all itemsets that have utility larger than a user specified value of minimum utility. In A Bottom-
Up Projection Based Algorithm for Mining High Utility Itemsets [12], Alva et al proposed the
CTU-PRO algorithm to mine the complete set of high utility itemsets from both sparse and
relatively dense datasets with short or long high utility patterns.
In the article Data Mining: A Tool for the Enhancement of Banking Sector [1], Shipra et al
described that data mining can be a very powerful and helpful tool to extract important and useful
information for banking sector from the historical as well as from the current data. Data mining
can be used in various fields of banking like Market segmentation by which banks can segment
their customers into different groups, direct mail marketing can help the banks to improve their
marketing strategy and to increase their business, customer churn to increase the rate of retention
of the customers, risk management to reduce the various risks like creditworthiness and fraud
detection to reduce the number of fraudulent.
In [7], the authors have presented a novel utility FP-tree by utilizing a tree structure for storing
essential information about frequent patterns for mining high utility itemsets. Higher efficiency in
mining high utility patterns is realized by implementing two important concepts. One is the
construction of the utility FP-tree and the other one is the mining of utility itemsets from the
utility FP-tree. The utility FP-tree-based pattern mining utilizes the pattern growth method to
avoid the costly generation of a large number of candidate sets and reduces the search space
dramatically. The experimentation was carried out using real life datasets and the results show
that the proposed approach effectively mines utility itemsets from large databases.
Jyothi et al proposed a High Utility Rare Itemset Mining [HURI] algorithm [6], to find those rare
itemsets, which are of high utility according to users’ preferences. Using HURI algorithm, high
utility rare itemsets are generated in two phases:-
(i)In first phase, rare itemsets are generated by considering those itemsets which have support
value less than the maximum support threshold.
(ii)In second phase, by inputting the utility threshold value according to users’ interest, rare
itemsets having utility value greater than the minimum utility threshold are generated.
HURI can produce high utility rare itemsets based on support threshold, utility threshold and
users’ interest.
4. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.2, No.2, April 2012
82
3. PROBLEM DEFINITION
First theoretical related concepts of the proposed algorithm, CSHURI, are described.
DEFINITION 3.1 (Utility Mining) Utility Mining finds all itemsets in transaction database with
utility values higher than the user defined minimum utility threshold.
Let I be a set of quantities of items I={i1, i2, i3,… , im}and D be a set of transactions
{T1,T2,…,Tn} with items, where each item i ε I(table 1). Each transaction in D is assigned a
transaction identifier (T_ID). The set of utilities is defined as U={u1, u2, u3,… , uk} (table 2). For
e.g. in transaction T19, the quantities of items A, B, C, D, E… are 0,0,0,1,2,… respectively.
The utility of an itemset X, i.e., u(X), is the sum of the utilities of itemset X in all the transactions
containing X. An itemset X is called a high utility itemset if and only if u(X) >= min_utility, where
min_utility is a user-defined minimum utility threshold [14]. Identification of the itemsets with
high utilities is called as Utility Mining [15].
DEFINITION 3.2 (Utility Table) A utility table UT (table 2) is a table containing items and
their corresponding utility values where each item i has some utility value uj in U={u1, u2, u3,… ,
uk } for some k > 0.
For example utility of item E is u(E) = 7 in (table 2).
DEFINITION 3.3 (Internal Utility) The internal utility value of item ip in a transaction Tq,
denoted o(ip, Tq) is the value of an item ip in a transaction Tq (Table 2). The internal utility reflects
the occurrence of the item in a transaction database.
In table 1, internal utility of item A in transaction T1 is o(A, T1) = 1, while internal utility of
item A in Transaction dataset D is o(A, D) = 21.
DEFINITION 3.4 (External Utility) The external utility value of an item is a numerical value
s(ip) associated with an item ip such that s(ip )=u(ip), where u is a utility function, a function
relating specific values in a domain according to user preferences (table 2).
From table 3, external utility of item A is s(A) = u(A) = 4.
DEFINITION 3.5 (Item Utility) The utility of an item ip in a transaction Tq, denoted U(ip, Tq) is
product of o(ip, Tq) and s(ip), where o(ip, Tq) is the internal utility value of ip, s(ip) is the external
utility value of ip(table 3) .
For eg., total utility of item A is U(A) = s(A) * o(A) = 4 * 21 = 84 (table 2).
DEFINITION 3.6 (Transaction Utility) The transaction utility value of a transaction, denoted
as U(Tq) is the sum of utility values of all items in a transaction Tq (table 1, table 2). The
transaction utility reflects the utility in a transaction database.
From Table 1 and Table 3, the transaction utility of the transaction T1, U(T1) =
U(A)+U(B)+U(C)+U(D)+ … + U(T)=39.
DEFINITION 3.7 (Rare Itemset Mining) Rare itemsets are the itemsets that occur infrequently
in the transaction data set. In many practical situations, the rare combinations of items in the
itemset with high utilities provide very useful insights to the user. Some infrequent patterns may
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also suggest the occurrence of interesting rare events or exceptional situations in the data. For e.g.
If {Fire=Yes} is frequent but {Fire=Yes, Alarm=ON} is infrequent, then latter is an interesting
infrequent pattern because it may indicate faulty alarm system [10].
Rare itemset mining is a challenging task. The key issues in mining rare itemsets are: -
(i) How to identify interesting rare patterns and
(ii) How to efficiently discover them in large datasets.
4. Proposed Algorithm
In [6], authors propose a High Utility Rare Itemset Mining [HURI] algorithm for generating high
utility rare itemsets of users’ interest. One more very interesting and innovative idea is to use
HURI as a base for customer utility mining. The proposed CSHURI algorithm, Customer
Segmentation using HURI, finds customers who purchase high profitable rare items and
accordingly classify the customers for providing good customers’ service.
Algorithm CSHURI
Description: Finding High Utility Rare Itemsets of users’ interest and classifying customers
Ck: Candidate itemset of size k
Lk: Rare itemset of size k
For each transaction t in database
begin
increment support for each item i present in t
End
L1= {Rare 1-itemset with support less than user provided max_sup}
for(k= 1; Lk!=Ø; k++)
begin
C k+1= candidates generated from Lk;
//loop to calculate total utility of each item
For each transaction t in database
begin
Calculate total quantity of each item i in t
Find total utility for item i using following formula:-
u(i,t) = quantity[i] * user_provided_utility for i
End
//loop to find rare itemsets and their utility
For each transaction t in database
begin
increment the count of all candidates in Ck+1 that are contained in t
Lk+1 = candidates in Ck+1 less than min_support
Add Lk+1 to the Itemset_Utility table in database by calculating rare itemset utility using formula:
Utility(R,t) = Σfor each individual item i in R (u(i,t));
End
//loop to find high utility rare itemset
For each itemset iset in rare itemset table R
begin
If (Utility(iset) > user_provided_threshold_for_high_utility_rare_itemset)
then iset is a rare_itemset that is of user interest i.e.high_utility_rare_itemset
else iset is a rare itemset but is not of user interest
End
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//loop to calculate profit of each transaction and use this profit as base for customer_utility_mining
For each transaction t in database
begin
Set profit of each transaction t in customer utility table as
Profit_transaction_t = (utility of each item i) * (quantity of item i in t)
If (Profit_transaction_t > user_provided_cust_utility)
Customer is a premium customer
Else
Customer is a general customer
End
Return high_utility_rare_itemsets, premium_customers
END
Figure 1 : Pseudo Code for CSHURI
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Table 3: Rare Itemset Table Table 5: Customer Transaction table
Table 4: High Utility Rare Itemset Table
Given a user-specified maximum support threshold maxsup, we are interested in a rule X if
sup(X) < maxsup. Rare rules are those rules appearing below the maximum support value. By
setting the value of maximum support threshold to 40%, the rare itemsets generated from table
1are listed in table 3. Rare itemsets of users’ interest or high utility rare itemsets fall below a
maximum support value but above a user provided high utility threshold. If high utility threshold
is set as 45, the high utility rare itemsets generated are listed in table 4.
HURI algorithm produces high utility rare itemsetst according to users’ interest. CSHURI, the
modified version of HURI, is used as a base for customer utility mining. Customer utility mining
Rare
itemsets
List of rare itemsets
Itemset
Utility
1-itemset
{D} 24
{G} 60
{H} 13
{S} 11
2-itemset
{D,G} 84
{D, H} 37
{D,S} 35
{G,H} 73
{G,S} 71
{H,S} 24
3-itemset
{D,G,H} 97
{D,G,S} 95
{G,H,S} 84
{D,H,S} 48
4-itemset {D,G, H,S} 108
Trans id
Customer
_id
Customer_n
ame
Customer_
type
Transaction_
Profit
1 c01 CA General 39
2 c02 CB General 44
3 c03 CC General 30
4 c04 CD Premium 47
5 c05 CE General 21
6 c06 CF General 10
7 c07 CG General 13
8 c08 CH General 35
9 c09 CI Premium 49
10 c10 CJ Premium 49
11 c11 CK General 31
12 c12 CL General 40
13 c13 CM General 23
14 c14 CN Premium 71
15 c15 CO General 31
16 c16 CJ General 2
17 c17 CK General 21
18 c18 CL General 41
19 c19 CM General 25
20 c20 CN General 17
21 c21 CO General 42
22 c22 CP General 13
23 c23 CQ General 32
24 c24 CR General 17
25 c25 CS Premium 48
26 c26 CT General 19
27 c27 CU General 29
28 c28 CJ General 30
29 c29 CV Premium 50
30 c30 CW General 13
31 c31 CX General 32
32 c32 CY General 13
33 c33 CZ General 38
34 c34 AV General 9
35 c35 AB General 32
Rare
itemsets
List of high utility rare
itemsets
Utility
1-itemset {G} 60
2-itemset
{D,G} 84
{G,H} 73
{G,S} 71
3-itemset
{D,G,H} 97
{D,G,S} 95
{G,H,S} 84
4-itemset {D,G, H,S} 108
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aims at finding customers who purchase high profitable rare items. This type of customers gives
almost hundred percent contributions towards the overall profit of the transaction. For e.g., by
setting the user provided customer utility as 45, customers can be classified as Premium (Profit
transaction > 45) or General Customer (table 5). After finding this type of customer, the
customers can be categorized and accordingly gold or silver cards can be issued for this type of
customers or new scheme can be introduced for them.
CSHURI algorithm, Customer Segmentation using HURI, uses two-phase HURI algorithm [6]
for finding customers who purchase high utility rare itemsets, after generating high utility rare
itemsets. In CSHURI Algorithm (Figure 1), customer classification is done through three
phases:-
(i) In first phase, rare itemsets are generated by considering those itemsets which have
support value less than the maximum support threshold (e.g. table 3).
(ii) In second phase, by inputting the utility threshold value according to users’ interest, rare
itemsets having utility value greater than the minimum utility threshold are generated
(e.g. table 4).
(iii)Finally in the last phase, customers are classified according the type of items being
purchased. By setting the customer utility threshold, all transactions having profit greater
than customer utility threshold are found and accordingly customers of corresponding
transactions are classified (e.g. table 5).
4. CONCLUSIONS AND FUTURE SCOPE
In retail markets, the customized service is the most crucial phase. Customer base is the prime
objective of customer relationship management. The key to attain this objective is to understand
the buying behaviour of customers. Clear Customer understanding requires properly focused
customer segmentation and actions to maximize customer retention, loyalty and profitability.
Data mining techniques are nowadays used to predict buying behaviour of customers by
analyzing large amounts of customer and transaction data. Stores can use its customer base as a
bargaining power to strike discount deals. Customer Utility mining can generate insights, which
can lead to effective customer segmentation in retail marketing. The proposed CSHURI
algorithm, Customer Segmentation using HURI, finds customers purchasing high utility rare
itemsets and accordingly customers are segmented easily. These customers provide maximum
profit to the overall transaction scenario. Marketers can then make business strategies by
providing special services to premium customers to delight the customers and retain the customer
with same business. Premium customers can be given special privileges, credit facilities to
customers, special cards, discount offer, promotion packages.
Enterprises can also use data mining to minimize purchasing costs; score suppliers by rating the
quality of their goods and services; identify the most effective promotions; identify profitable
itemsets. Also after identification of high utility rare itemsets, marketers can do the promotion or
advertising of such itemsets to increase the overall profit of the business. The association of
customers with different products can be found with the help of data mining systems. The
knowledge generated from CSHURI will be useful for retail businesses in decision-making
process. The outcome of CSHURI would enable the top management or business analyst in
crucial decision-making such as catalog design, providing credit facility, cross marketing,
finalizing discount policy, analyzing consumers’ buying behaviour, organizing shelf space, loss-
leader analysis and quality improvement in supermarket.
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Authors
Mrs. Jyothi Pillai is Associate Professor in Department of Computer Applications
at Bhilai Institute of Technology, Durg (C.G.), India. She is a post-graduate from
Barkatullah University, India. She is a Life member of Indian Society for Technical
Education. She has a total teaching experience of 16½ years. She has a total of 15
Research papers published in National / International Journals / Conferences into
her credit. Presently, she is pursuing Ph.D. from Pt. Ravi Shankar Shukla
University, Raipur under the guidance of Dr. O.P.Vyas, IIIT, Allahabad.
Dr.O.P.Vyas is currently working as Professor and Incharge Officer (Doctoral
Research Section) in Indian Institute of Information Technology-Allahabad (Govt.
of India’s Center of Excellence in I.T.). Dr.Vyas has done M.Tech.(Computer
Science) from IIT Kharagpur and has done Ph.D. work in joint collaboration with
Technical University of Kaiserslautern (Germany) and I.I.T.Kharagpur. With more
than 25 years of academic experience Dr.Vyas has guided Four Scholars for the
successful award of Ph.D. degree and has more than 80 research publications with
two books to his credit. His current research interests are Linked Data Mining and
Service Oriented Architectures.