This document provides an introduction to the concept of data mining. It discusses several applications of data mining such as credit ratings, targeted marketing, fraud detection, and customer relationship management. It then defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. The document outlines some common data mining techniques including classification, clustering, association rule mining, and collaborative filtering. It provides examples of how these techniques can be applied and discusses their advantages and disadvantages.
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. It describes the data mining process of problem formulation, data collection/preprocessing, mining methods, and result evaluation. Specific mining methods covered include classification, clustering, association rule mining, and neural networks. It also discusses applications of data mining across various industries and gives some examples of successful real-world data mining implementations.
- The document discusses mathematical methods for tensor factorization applied to recommender systems.
- Tensor factorization techniques can model additional contextual information that standard matrix factorization cannot capture. This allows the recommendations to be more personalized.
- Two main tensor factorization methods discussed are Higher-Order Singular Value Decomposition (HOSVD) and PARAllel FACtor analysis (PARAFAC).
- HOSVD generalizes singular value decomposition to tensors. PARAFAC decomposes a tensor into a sum of rank-one tensors. Both aim to discover latent factors between user data.
This document summarizes mathematical methods of tensor factorization applied to recommender systems. It discusses motivations and contributions, information overload and recommender systems, matrix and tensor factorization techniques in recommender system literature such as matrix factorization, singular value decomposition, high-order singular value decomposition, and parallel factor analysis. It also covers challenges in context-aware recommender systems and proposed solutions to incorporate contextual information.
This document discusses knowledge discovery and data mining. It defines knowledge discovery as the process of automatically searching large volumes of data for patterns that can be considered knowledge. Data mining is defined as one step in the knowledge discovery process and involves using computational methods to discover patterns in large datasets. The document outlines common data mining tasks such as predictive tasks, descriptive tasks, and anomaly detection. It also discusses evaluating data mining algorithms, including assessing the performance of a single algorithm and comparing the performance of multiple algorithms.
The document describes the 8 step data mining process:
1) Defining the problem, 2) Collecting data, 3) Preparing data, 4) Pre-processing, 5) Selecting an algorithm and parameters, 6) Training and testing, 7) Iterating models, 8) Evaluating the final model. It discusses issues like defining classification vs estimation problems, selecting appropriate inputs and outputs, and determining when sufficient data has been collected for modeling.
Machine Learning - Algorithms and simple business casesClaudio Mirti
Linear regression, logistic regression, and decision trees are commonly used supervised learning algorithms. Linear regression models the relationship between input and output variables to predict future values, logistic regression is used for binary classification tasks, and decision trees split data into branches to make predictions. Unsupervised learning algorithms like k-means clustering group unlabeled data into clusters with similar characteristics. Reinforcement learning optimizes strategies through trial-and-error interactions like optimizing inventory levels or self-driving cars. Convolutional neural networks in deep learning can diagnose diseases from scans, detect logos in images, and understand customer perception through visual data analysis.
This brief work is aimed in the direction of basics of data sciences and model building with focus on implementation on fairly sizable dataset. It focuses on cleaning the data, visualization, EDA, feature scaling, feature normalization, k-nearest neighbor, logistic regression, random forests, cross validation without delving too deep into any of them but giving a start to a new learner.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. It describes the data mining process of problem formulation, data collection/preprocessing, mining methods, and result evaluation. Specific mining methods covered include classification, clustering, association rule mining, and neural networks. It also discusses applications of data mining across various industries and gives some examples of successful real-world data mining implementations.
- The document discusses mathematical methods for tensor factorization applied to recommender systems.
- Tensor factorization techniques can model additional contextual information that standard matrix factorization cannot capture. This allows the recommendations to be more personalized.
- Two main tensor factorization methods discussed are Higher-Order Singular Value Decomposition (HOSVD) and PARAllel FACtor analysis (PARAFAC).
- HOSVD generalizes singular value decomposition to tensors. PARAFAC decomposes a tensor into a sum of rank-one tensors. Both aim to discover latent factors between user data.
This document summarizes mathematical methods of tensor factorization applied to recommender systems. It discusses motivations and contributions, information overload and recommender systems, matrix and tensor factorization techniques in recommender system literature such as matrix factorization, singular value decomposition, high-order singular value decomposition, and parallel factor analysis. It also covers challenges in context-aware recommender systems and proposed solutions to incorporate contextual information.
This document discusses knowledge discovery and data mining. It defines knowledge discovery as the process of automatically searching large volumes of data for patterns that can be considered knowledge. Data mining is defined as one step in the knowledge discovery process and involves using computational methods to discover patterns in large datasets. The document outlines common data mining tasks such as predictive tasks, descriptive tasks, and anomaly detection. It also discusses evaluating data mining algorithms, including assessing the performance of a single algorithm and comparing the performance of multiple algorithms.
The document describes the 8 step data mining process:
1) Defining the problem, 2) Collecting data, 3) Preparing data, 4) Pre-processing, 5) Selecting an algorithm and parameters, 6) Training and testing, 7) Iterating models, 8) Evaluating the final model. It discusses issues like defining classification vs estimation problems, selecting appropriate inputs and outputs, and determining when sufficient data has been collected for modeling.
Machine Learning - Algorithms and simple business casesClaudio Mirti
Linear regression, logistic regression, and decision trees are commonly used supervised learning algorithms. Linear regression models the relationship between input and output variables to predict future values, logistic regression is used for binary classification tasks, and decision trees split data into branches to make predictions. Unsupervised learning algorithms like k-means clustering group unlabeled data into clusters with similar characteristics. Reinforcement learning optimizes strategies through trial-and-error interactions like optimizing inventory levels or self-driving cars. Convolutional neural networks in deep learning can diagnose diseases from scans, detect logos in images, and understand customer perception through visual data analysis.
This brief work is aimed in the direction of basics of data sciences and model building with focus on implementation on fairly sizable dataset. It focuses on cleaning the data, visualization, EDA, feature scaling, feature normalization, k-nearest neighbor, logistic regression, random forests, cross validation without delving too deep into any of them but giving a start to a new learner.
Data mining involves finding hidden patterns in large datasets. It differs from traditional data access in that the query may be unclear, the data has been preprocessed, and the output is an analysis rather than a data subset. Data mining algorithms attempt to fit models to the data by examining attributes, criteria for preference of one model over others, and search techniques. Common data mining tasks include classification, regression, clustering, association rule learning, and prediction.
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
The document discusses data mining primitives, languages, and system architectures. It introduces key concepts like data mining tasks, knowledge types, interestingness measures, and presentation formats. It also presents DMQL, a data mining query language that allows mining different knowledge types from relational databases and data warehouses. Finally, it outlines four architectures for data mining systems - no coupling, loose coupling, semi-tight coupling, and tight coupling with database and data warehouse systems.
This technical report explores using set-valued attributes for decision tree induction algorithms. Conventional algorithms use single-valued attributes, but the authors argue set-valued attributes can improve accuracy and speed. They describe modifying decision tree algorithms for splitting, pruning, and classification when attributes can have set values. Experiments show the proposed approach works well with only simple pre-pruning needed to limit excessive instance replication across tree branches. The set-valued approach is intended to better handle noise and variability in data values.
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
The document discusses various applications of dimension reduction techniques to extract low-dimensional representations from high-dimensional data for purposes of prediction, descriptive analysis, and input into subsequent causal analysis. It provides examples of such applications using Google search data, genetic data, medical claims data, credit scores, online purchases, and congressional roll call votes. It also discusses issues around text as data, including bag-of-words representations and the use of automated and manual steps in text analysis.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
BW article on professional respondents 2-23 (1)Brett Watkins
This document discusses issues with professional respondents in qualitative research and proposes solutions. It analyzes the costs of different recruitment methods, finding that database recruitment is much more cost-effective than list recruitment due to higher response rates. It argues that becoming more adversarial towards database members would reduce cooperation rates and drive up costs. Instead, it suggests that advanced database technologies can improve quality by validating member data, identifying duplicative or suspicious entries, and flagging professional respondents without their knowledge. This allows for easier database registration to attract more members while still screening out cheaters.
The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in ...IJMIT JOURNAL
Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model.
It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting appropriate features and building optimum decision trees. The new proposed hybrid classification model is established based on a combination of clustering, feature selection, decision trees, and genetic algorithm
techniques. We used clustering and feature selection techniques to pre-process the input samples to construct the decision trees in the credit scoring model. The proposed hybrid model choices and combines the best decision trees based on the optimality criteria. It constructs the final decision tree for credit scoring of customers. Using one credit dataset, results confirm that the classification accuracy of the proposed hybrid classification model is more than almost the entire classification models that have been compared in this paper. Furthermore, the number of leaves and the size of the constructed decision tree (i.e. complexity) are less, compared with other decision tree models. In this work, one financial dataset was chosen for experiments, including Bank Mellat credit dataset.
A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document IJECEIAES
Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
This chapter discusses exploratory research design using secondary data sources. It begins with an overview and outlines the key points that will be covered, including the differences between primary and secondary data, criteria for evaluating secondary data, and classifications of various secondary data sources. Examples of secondary data sources discussed include internal business data, published materials from businesses and governments, computerized databases, and syndicated data services providing household and institutional data.
Distributed Representation-based Recommender Systems in E-commerceRakuten Group, Inc.
Intelligence Domain Group, Rakuten Institute of Technology is working on developing various kinds of solutions utilizing Rakuten Data in order to assist Rakuten services.
In this presentation, we propose a novel item recommender algorithm based on distributed representation. We confirmed that performance of the proposed algorithm outperformed conventional recommender algorithms such as collaborative filtering and matrix factorization.
The document discusses using social network data, specifically tweets, to predict stock market movements. It outlines the general methodology, which includes collecting tweet data from APIs, filtering relevant tweets, preprocessing the text through normalization, noise removal, and feature extraction. Topic modeling and sentiment analysis are then used to extract topics and sentiment from tweets. These extracted features along with tweet metadata are then used to construct prediction models using classifiers like SVM and linear regression. The models are trained and tested using windowing to correlate sentiment and topic features from past tweets to subsequent stock price movements. Accuracy of these predictions and future areas of improvement are also discussed.
Intent-Aware Temporal Query Modeling for Keyword SuggestionFindwise
This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent user intent) is represented using hypergraphs which allows us to explore more complex relationships compared to graph-based approaches. This time-varying dependence is modeled using the framework of probabilistic graphical models. The inferred interactions are used for query keyword suggestion - a key task in web information retrieval. Preliminary experiments using query logs collected from internal search engine of a large health care organization yield promising results. In particular, our model is able to capture temporal variations between queries relationships that reflect known trends in disease occurrence. Further, hypergraph-based modeling captures relationships significantly better compared to graph-based approaches.
Dwdm ppt for the btech student contain basisnivatripathy93
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. The document defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. It outlines some common data mining applications and techniques, including classification, clustering, association rule mining, and collaborative filtering. The document also compares data mining to related fields and discusses how the knowledge discovery process works.
The document discusses various data mining techniques including association rules, classification, clustering, and approaches to discovering patterns in datasets. It covers clustering algorithms like partition and hierarchical clustering. It also explains different data mining problems like discovering sequential patterns, patterns in time series data, and classification and regression rules.
Data mining involves discovering patterns from large amounts of data. It can be used for applications like credit ratings, targeted marketing, fraud detection, and customer relationship management. Some common data mining techniques include classification, clustering, regression, and association rule mining. Decision trees are a popular classification technique that uses a tree structure with internal nodes representing attributes and leaf nodes representing target classes.
Dear Sir/Ma’am
I am interested to work as a data specialist in your organization. I believe my experience, skills and work attitude will aid your organization in a great way. Please accept my enclosed resume with this letter.
I worked at Accenture for the last four years. My key responsibilities here were to collect, analyse, store and create data. I made sure that these data were accurate and not damaged. As far as my educational background is concerned, I have a bachelor's degree in EXTC. I am excellent at solving problems and have great analytical skills. I am capable of working well with network administration and can explain the technical problems.
I would appreciate if we could meet up for an interview wherein we can discuss more on this. I can be contacted at +919493377607 or you can email me at imtiaz.khan.sw39@gmail.com
Thank You.
Yours sincerely,
Imtiaz Khan
A lot of people talk about Data Mining, Machine Learning and Big Data. It clearly must be important, right?
A lot of people are also trying to sell you snake oil - sometimes half-arsed and overpriced products or solutions promising a world of insight into your customers or users if you handover your data to them. Instead, trying to understanding your own data and what you could do with it, should be the first thing you’d be looking at.
In this talk, we’ll introduce some basic terminology about Data and Text Mining as well as Machine Learning and will have a look at what you can on your own to understand more about your data and discover patterns in your data.
The document discusses data mining primitives, languages, and system architectures. It introduces key concepts like data mining tasks, knowledge types, interestingness measures, and presentation formats. It also presents DMQL, a data mining query language that allows mining different knowledge types from relational databases and data warehouses. Finally, it outlines four architectures for data mining systems - no coupling, loose coupling, semi-tight coupling, and tight coupling with database and data warehouse systems.
This technical report explores using set-valued attributes for decision tree induction algorithms. Conventional algorithms use single-valued attributes, but the authors argue set-valued attributes can improve accuracy and speed. They describe modifying decision tree algorithms for splitting, pruning, and classification when attributes can have set values. Experiments show the proposed approach works well with only simple pre-pruning needed to limit excessive instance replication across tree branches. The set-valued approach is intended to better handle noise and variability in data values.
Delayed Rewards in the context of Reinforcement Learning based Recommender ...Debmalya Biswas
We present a Reinforcement Learning (RL) based approach to implement Recommender systems. The results are based on a real-life Wellness app that is able to provide personalized health / activity related content to users in an interactive fashion. Unfortunately, current recommender systems are unable to adapt to continuously evolving features, e.g. user sentiment, and scenarios where the RL reward needs to computed based on multiple and unreliable feedback channels (e.g., sensors, wearables). To overcome this, we propose three constructs: (i) weighted feedback channels, (ii) delayed rewards, and (iii) rewards boosting, which we believe are essential for RL to be used in Recommender Systems.
The document discusses various applications of dimension reduction techniques to extract low-dimensional representations from high-dimensional data for purposes of prediction, descriptive analysis, and input into subsequent causal analysis. It provides examples of such applications using Google search data, genetic data, medical claims data, credit scores, online purchases, and congressional roll call votes. It also discusses issues around text as data, including bag-of-words representations and the use of automated and manual steps in text analysis.
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
—Recommendation is crucial in both academia andindustry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic re-gression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies sufferfrom two limitations: (1) considering the recommendation asa static procedure and ignoring the dynamic interactive naturebetween users and the recommender systems; (2) focusing on theimmediate feedback of recommended items and neglecting thelong-term rewards. To address the two limitations, in this paperwe propose a novel recommendation framework based on deepreinforcement learning, called DRR. The DRR framework treatsrecommendation as a sequential decision making procedure andadopts an “Actor-Critic” reinforcement learning scheme to modelthe interactions between the users and recommender systems,which can consider both the dynamic adaptation and long-term rewards. Further more, a state representation module isincorporated into DRR, which can explicitly capture the interac-tions between items and users. Three instantiation structures aredeveloped. Extensive experiments on four real-world datasets areconducted under both the offline and online evaluation settings.The experimental results demonstrate the proposed DRR methodindeed outperforms the state-of-the-art competitors
BW article on professional respondents 2-23 (1)Brett Watkins
This document discusses issues with professional respondents in qualitative research and proposes solutions. It analyzes the costs of different recruitment methods, finding that database recruitment is much more cost-effective than list recruitment due to higher response rates. It argues that becoming more adversarial towards database members would reduce cooperation rates and drive up costs. Instead, it suggests that advanced database technologies can improve quality by validating member data, identifying duplicative or suspicious entries, and flagging professional respondents without their knowledge. This allows for easier database registration to attract more members while still screening out cheaters.
The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in ...IJMIT JOURNAL
Decision tree modelling, as one of data mining techniques, is used for credit scoring of bank customers. The main problem is the construction of decision trees that could classify customers optimally. This study presents a new hybrid mining approach in the design of an effective and appropriate credit scoring model.
It is based on genetic algorithm for credit scoring of bank customers in order to offer credit facilities to each class of customers. Genetic algorithm can help banks in credit scoring of customers by selecting appropriate features and building optimum decision trees. The new proposed hybrid classification model is established based on a combination of clustering, feature selection, decision trees, and genetic algorithm
techniques. We used clustering and feature selection techniques to pre-process the input samples to construct the decision trees in the credit scoring model. The proposed hybrid model choices and combines the best decision trees based on the optimality criteria. It constructs the final decision tree for credit scoring of customers. Using one credit dataset, results confirm that the classification accuracy of the proposed hybrid classification model is more than almost the entire classification models that have been compared in this paper. Furthermore, the number of leaves and the size of the constructed decision tree (i.e. complexity) are less, compared with other decision tree models. In this work, one financial dataset was chosen for experiments, including Bank Mellat credit dataset.
A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document IJECEIAES
Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset.
USING NLP APPROACH FOR ANALYZING CUSTOMER REVIEWScsandit
The Web considers one of the main sources of customer opinions and reviews which they are represented in two formats; structured data (numeric ratings) and unstructured data (textual comments). Millions of textual comments about goods and services are posted on the web by customers and every day thousands are added, make it a big challenge to read and understand them to make them a useful structured data for customers and decision makers. Sentiment
analysis or Opinion mining is a popular technique for summarizing and analyzing those opinions and reviews. In this paper, we use natural language processing techniques to generate some rules to help us understand customer opinions and reviews (textual comments) written in the Arabic language for the purpose of understanding each one of them and then convert them to a structured data. We use adjectives as a key point to highlight important information in the text then we work around them to tag attributes that describe the subject of the reviews, and we associate them with their values (adjectives).
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
This chapter discusses exploratory research design using secondary data sources. It begins with an overview and outlines the key points that will be covered, including the differences between primary and secondary data, criteria for evaluating secondary data, and classifications of various secondary data sources. Examples of secondary data sources discussed include internal business data, published materials from businesses and governments, computerized databases, and syndicated data services providing household and institutional data.
Distributed Representation-based Recommender Systems in E-commerceRakuten Group, Inc.
Intelligence Domain Group, Rakuten Institute of Technology is working on developing various kinds of solutions utilizing Rakuten Data in order to assist Rakuten services.
In this presentation, we propose a novel item recommender algorithm based on distributed representation. We confirmed that performance of the proposed algorithm outperformed conventional recommender algorithms such as collaborative filtering and matrix factorization.
The document discusses using social network data, specifically tweets, to predict stock market movements. It outlines the general methodology, which includes collecting tweet data from APIs, filtering relevant tweets, preprocessing the text through normalization, noise removal, and feature extraction. Topic modeling and sentiment analysis are then used to extract topics and sentiment from tweets. These extracted features along with tweet metadata are then used to construct prediction models using classifiers like SVM and linear regression. The models are trained and tested using windowing to correlate sentiment and topic features from past tweets to subsequent stock price movements. Accuracy of these predictions and future areas of improvement are also discussed.
Intent-Aware Temporal Query Modeling for Keyword SuggestionFindwise
This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent user intent) is represented using hypergraphs which allows us to explore more complex relationships compared to graph-based approaches. This time-varying dependence is modeled using the framework of probabilistic graphical models. The inferred interactions are used for query keyword suggestion - a key task in web information retrieval. Preliminary experiments using query logs collected from internal search engine of a large health care organization yield promising results. In particular, our model is able to capture temporal variations between queries relationships that reflect known trends in disease occurrence. Further, hypergraph-based modeling captures relationships significantly better compared to graph-based approaches.
Dwdm ppt for the btech student contain basisnivatripathy93
This document provides an introduction to data mining. It discusses why organizations use data mining, such as for credit ratings, fraud detection, and customer relationship management. The document defines data mining as the process of analyzing large databases to find valid, novel, useful, and understandable patterns. It outlines some common data mining applications and techniques, including classification, clustering, association rule mining, and collaborative filtering. The document also compares data mining to related fields and discusses how the knowledge discovery process works.
The document discusses various data mining techniques including association rules, classification, clustering, and approaches to discovering patterns in datasets. It covers clustering algorithms like partition and hierarchical clustering. It also explains different data mining problems like discovering sequential patterns, patterns in time series data, and classification and regression rules.
Data mining involves discovering patterns from large amounts of data. It can be used for applications like credit ratings, targeted marketing, fraud detection, and customer relationship management. Some common data mining techniques include classification, clustering, regression, and association rule mining. Decision trees are a popular classification technique that uses a tree structure with internal nodes representing attributes and leaf nodes representing target classes.
Dear Sir/Ma’am
I am interested to work as a data specialist in your organization. I believe my experience, skills and work attitude will aid your organization in a great way. Please accept my enclosed resume with this letter.
I worked at Accenture for the last four years. My key responsibilities here were to collect, analyse, store and create data. I made sure that these data were accurate and not damaged. As far as my educational background is concerned, I have a bachelor's degree in EXTC. I am excellent at solving problems and have great analytical skills. I am capable of working well with network administration and can explain the technical problems.
I would appreciate if we could meet up for an interview wherein we can discuss more on this. I can be contacted at +919493377607 or you can email me at imtiaz.khan.sw39@gmail.com
Thank You.
Yours sincerely,
Imtiaz Khan
This document contains the course syllabus for a data warehousing, filtering, and mining lecture at Temple University. The key points are:
- The course will cover data warehousing, data mining techniques like classification, clustering, association rule mining.
- Grading will be based on homework assignments, quizzes, a class presentation, individual project, and final exam.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- The goal is to discuss efficient data analysis techniques for strategic decision making from large databases.
This document provides a summary of a course syllabus for a data warehousing and mining course. The key details include:
- The course meets on Tuesdays from 4:40-7:10pm and is taught by Professor Slobodan Vucetic.
- The objective is to discuss data management techniques like data warehouses, data marts, and online analytical processing (OLAP) for efficient data analysis.
- Topics include data warehousing, OLAP, data preprocessing, association rules, classification, clustering, and mining complex data types.
- Grading will be based on homework, quizzes, a class presentation, individual project, and a final exam.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
Chapter 4 Classification in data sience .pdfAschalewAyele2
This document discusses data mining tasks related to predictive modeling and classification. It defines predictive modeling as using historical data to predict unknown future values, with a focus on accuracy. Classification is described as predicting categorical class labels based on a training set. Several classification algorithms are mentioned, including K-nearest neighbors, decision trees, neural networks, Bayesian networks, and support vector machines. The document also discusses evaluating classification performance using metrics like accuracy, precision, recall, and a confusion matrix.
Challenges in building a churn prediction model in different industries, presented by Jelena Pekez from Comtrade System Integration. Talk is focused on real-life use-case experience.
The document discusses how machine learning algorithms can be used in ecommerce to increase sales and conversions. It provides an overview of common algorithms such as K-means clustering which can be used to segment customers into personas for targeted marketing. K-nearest neighbors algorithm can be used to generate personalized product recommendations based on a user's purchase history and preferences of similar customers. Examples are given of how these algorithms work and practical tips provided for implementing machine learning in ecommerce applications.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and identify patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk assessment, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
Data mining is the process of automatically discovering useful information from large data sets. It draws from machine learning, statistics, and database systems to analyze data and discover patterns. Common data mining tasks include classification, clustering, association rule mining, and sequential pattern mining. These tasks are used for applications like credit risk analysis, fraud detection, customer segmentation, and market basket analysis. Data mining aims to extract unknown and potentially useful patterns from large data sets.
The document provides an overview of data mining techniques and related concepts. It defines data mining and compares it to knowledge discovery in databases (KDD). It discusses the basic data mining tasks of classification, clustering, association rule mining, and summarization. It also covers related areas like databases, statistics, machine learning, and visualization techniques used in data mining. Finally, it provides an overview of common data mining techniques including decision trees, neural networks, genetic algorithms, and others.
The document discusses data warehousing, data mining, and business intelligence applications. It explains that data warehousing organizes and structures data for analysis, and that data mining involves preprocessing, characterization, comparison, classification, and forecasting of data to discover knowledge. The final stage is presenting discovered knowledge to end users through visualization and business intelligence applications.
The document discusses data mining and knowledge discovery in databases. It defines data mining as extracting patterns from large amounts of data. The key steps in the knowledge discovery process are presented as data selection, preprocessing, data mining, and interpretation. Common data mining techniques include clustering, classification, and association rule mining. Clustering groups similar data objects, classification predicts categorical labels, and association rules find relationships between variables. Data mining has applications in many domains like market analysis, fraud detection, and bioinformatics.
This document provides an overview of data mining and knowledge discovery in databases. It discusses why data mining is needed due to large volumes of data, describes the data mining process including data preparation, transformation, mining methods and model evaluation. Specific data mining techniques discussed include association rule mining to find frequent patterns in transactional data and decision tree learning as a supervised learning method to classify instances.
The document introduces data mining and knowledge discovery in databases. It discusses why data mining is needed due to large datasets that cannot be analyzed manually. It also covers the data mining process, common data mining techniques like association rules and decision trees, applications of data mining in various domains, and some popular data mining tools.
The document discusses massively parallel cloud data storage systems and NoSQL databases. It describes why these systems were developed due to the large data needs of social media and web companies. It then covers key aspects of NoSQL databases like their flexible schemas, distributed nature, and focus on high availability over consistency through eventual consistency. Common NoSQL systems and their architectures are also outlined.
This document describes an Arduino-based home automation system using Bluetooth. The system allows users to control electrical appliances in their home remotely from an Android smartphone. An Arduino board is interfaced with a Bluetooth module to receive ON/OFF commands from a GUI app on the phone. Loads like lights and fans are then controlled by the Arduino board through optoisolators and thyristors. The system provides convenience and energy savings by allowing remote control of devices without needing to move proximity to switches. It was experimentally tested to successfully control sample appliances from a wireless mobile device.
The Apriori algorithm is used to find frequent itemsets and generate association rules. It works in multiple passes over the transactional database: (1) Find frequent items in the database and derive frequent itemsets with a length of 1, (2) Join frequent itemsets from the previous pass to get candidate itemsets of the next length, (3) Prune the candidates that have a subset that is infrequent, (4) Count the support for remaining candidates and output frequent itemsets. This process is repeated until no frequent itemsets are found. The frequent itemsets are then used to generate association rules that satisfy minimum support and confidence thresholds.
This document discusses various number systems and character encoding schemes used in digital circuits and computing including: 2's complement representation, binary coded decimal, Gray codes, and ASCII character encoding. It also discusses data storage in binary registers and processing data using digital logic circuits. The key topics covered are number representation, character encoding, data storage, and digital logic processing.
The document discusses the history and applications of the traveling salesman problem (TSP). It describes how the TSP involves finding the shortest route for a salesman to visit each city in a list only once before returning home. It provides examples of the TSP in route planning and ATM servicing. The document also outlines methods for exactly and approximately solving TSP instances, including using linear programming and heuristics. It gives examples of large TSPs that have been solved involving thousands of cities from real applications in logistics and circuit board design.
This document provides an overview of image processing. It defines image processing as any form of signal processing where the input is an image, such as photos or video frames, and the output can be another image or parameters related to the image. The document discusses applications of image processing like face detection and medical imaging. It also outlines different types of image processing, components used in image processing systems, and the future potential of image processing with more powerful computing. In conclusion, the document states that image processing techniques can enhance, analyze, and construct images for various applications.
This document discusses using artificial neural networks for cybersecurity and intrusion detection systems. It introduces artificial intelligence and neural networks, and how they can be used to identify and classify network activity based on limited data to protect against cyber threats. Neural networks are seen as crucial for cybersecurity, as they can help speed up detecting attacks, protect sites from attacks by identifying new threats quickly, and help prevent full-scale breaches. The future of AI in cybersecurity is discussed, with the potential for systems that can hunt for targeted attacks much faster and threat detection systems that can continuously learn and improve over time.
RAM allows stored data to be accessed randomly in any order. It is a type of volatile memory that does not permanently store data and loses its contents when powered off. There are two main types of RAM: static RAM and dynamic RAM. Dynamic RAM needs to be refreshed to maintain its contents while static RAM does not. RAM technologies have evolved from FPM DRAM to EDO DRAM, SDRAM, DDR SDRAM, and RDRAM to increase bandwidth and transfer rates. The memory hierarchy includes CPU registers, cache memory levels L1-L3, main memory, virtual memory, and storage. Future RAM technologies aim to be smaller, faster, and cheaper through innovations like RRAM and Z-RAM.
This document discusses various number coding systems including 2's complement, binary coded decimal, Gray code, and ASCII. It explains how these codes represent numeric and character data in binary and how data can be stored in registers. Addition and subtraction are demonstrated using 2's complement numbers. The document also introduces the concept of transferring data between registers using digital logic circuits to process the information.
The document discusses data warehousing, including its history, types, security, applications, components, architecture, benefits and problems. A data warehouse is defined as a subject-oriented, integrated, time-variant collection of data to support management decision making. In the 1990s, organizations needed timely data but traditional systems were too slow. Data warehouses now provide competitive advantages through improved decision making and productivity. They integrate data from multiple sources to support applications like customer analysis, stock control and fraud detection.
Events in UML include signals, calls, the passing of time, and state changes. There are four main types of events: signal events, call events, time events, and change events.
Signal events represent asynchronous communications between objects, with signals serving as parameters. Call events represent synchronous operation dispatches. Time events occur with the passage of time, modeled using "after." Change events represent a change in state or condition, modeled using "when."
State machines specify an object's sequence of states in response to events. States are represented as rectangles, and transitions between states are lines. State machines model behavior for objects responding to asynchronous stimuli or those with behavior dependent on past states.
This document discusses biomass conversion technologies for producing energy from waste. It defines biomass as living or recently living material from plants or animals not used for food or feed. There are three main methods for thermochemical conversion of biomass: combustion, gasification, and pyrolysis. Biochemical conversion methods like anaerobic digestion can produce biogas, which can be converted to power and heat. Specific technologies covered include anaerobic digestion, pyrolysis, transesterification of vegetable oils into biodiesel, and growing energy crops. The document concludes that large-scale biorefineries, improved catalysis technologies, and combined government/industry efforts can help increase the role of biomass in energy production today.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
2. Why Data Mining
Credit ratings/targeted marketing:
Given a database of 100,000 names, which persons are the
least likely to default on their credit cards?
Identify likely responders to sales promotions
Fraud detection
Which types of transactions are likely to be fraudulent, given the
demographics and transactional history of a particular
customer?
Customer relationship management:
Which of my customers are likely to be the most loyal, and
which are most likely to leave for a competitor? :
Data Mining helps extract such
information
3. Data mining
Process of semi-automatically analyzing
large databases to find patterns that are:
valid: hold on new data with some certainity
novel: non-obvious to the system
useful: should be possible to act on the item
understandable: humans should be able to
interpret the pattern
Also known as Knowledge Discovery in
Databases (KDD)
4. Applications
Banking: loan/credit card approval
predict good customers based on old customers
Customer relationship management:
identify those who are likely to leave for a competitor.
Targeted marketing:
identify likely responders to promotions
Fraud detection: telecommunications, financial
transactions
from an online stream of event identify fraudulent events
Manufacturing and production:
automatically adjust knobs when process parameter changes
5. Applications (continued)
Medicine: disease outcome, effectiveness of
treatments
analyze patient disease history: find relationship
between diseases
Molecular/Pharmaceutical: identify new drugs
Scientific data analysis:
identify new galaxies by searching for sub clusters
Web site/store design and promotion:
find affinity of visitor to pages and modify layout
6. The KDD process
Problem fomulation
Data collection
subset data: sampling might hurt if highly skewed data
feature selection: principal component analysis, heuristic
search
Pre-processing: cleaning
name/address cleaning, different meanings (annual, yearly),
duplicate removal, supplying missing values
Transformation:
map complex objects e.g. time series data to features e.g.
frequency
Choosing mining task and mining method:
Result evaluation and Visualization:
Knowledge discovery is an iterative process
7. Relationship with other
fields
Overlaps with machine learning, statistics,
artificial intelligence, databases, visualization
but more stress on
scalability of number of features and instances
stress on algorithms and architectures whereas
foundations of methods and formulations provided
by statistics and machine learning.
automation for handling large, heterogeneous data
10. Classification
Given old data about customers and
payments, predict new applicant’s loan
eligibility.
Age
Salary
Profession
Location
Customer type
Previous customers Classifier Decision rules
Salary > 5 L
Prof. = Exec
New applicant’s data
Good/
bad
11. Classification methods
Goal: Predict class Ci = f(x1, x2, .. Xn)
Regression: (linear or any other polynomial)
a*x1 + b*x2 + c = Ci.
Nearest neighour
Decision tree classifier: divide decision space
into piecewise constant regions.
Probabilistic/generative models
Neural networks: partition by non-linear
boundaries
12. Define proximity between instances, find
neighbors of new instance and assign
majority class
Case based reasoning: when attributes
are more complicated than real-valued.
Nearest neighbor
• Cons
– Slow during application.
– No feature selection.
– Notion of proximity vague
• Pros
+ Fast training
13. Tree where internal nodes
are simple decision rules on
one or more attributes and
leaf nodes are predicted
class labels.
Decision trees
Salary < 1 M
Prof = teacher
Good
Age < 30
BadBad
Good
14. Decision tree classifiers
Widely used learning method
Easy to interpret: can be re-represented as if-
then-else rules
Approximates function by piece wise constant
regions
Does not require any prior knowledge of data
distribution, works well on noisy data.
Has been applied to:
classify medical patients based on the disease,
equipment malfunction by cause,
loan applicant by likelihood of payment.
15. Pros and Cons of decision
trees
· Cons
- Cannot handle complicated
relationship between features
- simple decision boundaries
- problems with lots of missing
data
· Pros
+ Reasonable training
time
+ Fast application
+ Easy to interpret
+ Easy to implement
+ Can handle large
number of features
More information:
http://www.stat.wisc.edu/~limt/treeprogs.html
16. Neural network
Set of nodes connected by directed
weighted edges
Hidden nodes
Output nodes
x1
x2
x3
x1
x2
x3
w1
w2
w3
y
n
i
ii
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y
xwo
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Basic NN unit
A more typical NN
17. Neural networks
Useful for learning complex data like
handwriting, speech and image
recognition
Neural networkClassification tree
Decision boundaries:
Linear regression
18. Pros and Cons of Neural
Network
· Cons
- Slow training time
- Hard to interpret
- Hard to implement: trial
and error for choosing
number of nodes
· Pros
+ Can learn more complicated
class boundaries
+ Fast application
+ Can handle large number of
features
Conclusion: Use neural nets only if
decision-trees/NN fail.
19. Bayesian learning
Assume a probability model on generation of data.
Apply bayes theorem to find most likely class as:
Naïve bayes: Assume attributes conditionally
independent given class value
Easy to learn probabilities by counting,
Useful in some domains e.g. text
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21. Clustering
Unsupervised learning when old data with class
labels not available e.g. when introducing a new
product.
Group/cluster existing customers based on time
series of payment history such that similar
customers in same cluster.
Key requirement: Need a good measure of
similarity between instances.
Identify micro-markets and develop policies for
each
22. Applications
Customer segmentation e.g. for targeted
marketing
Group/cluster existing customers based on time
series of payment history such that similar customers
in same cluster.
Identify micro-markets and develop policies for each
Collaborative filtering:
group based on common items purchased
Text clustering
Compression
23. Distance functions
Numeric data: euclidean, manhattan distances
Categorical data: 0/1 to indicate
presence/absence followed by
Hamming distance (# dissimilarity)
Jaccard coefficients: #similarity in 1s/(# of 1s)
data dependent measures: similarity of A and B
depends on co-occurance with C.
Combined numeric and categorical data:
weighted normalized distance:
25. Partitional methods: K-
means
Criteria: minimize sum of square of distance
Between each point and centroid of the
cluster.
Between each pair of points in the cluster
Algorithm:
Select initial partition with K clusters:
random, first K, K separated points
Repeat until stabilization:
Assign each point to closest cluster center
Generate new cluster centers
Adjust clusters by merging/splitting
26. Collaborative Filtering
Given database of user preferences, predict
preference of new user
Example: predict what new movies you will like
based on
your past preferences
others with similar past preferences
their preferences for the new movies
Example: predict what books/CDs a person may
want to buy
(and suggest it, or give discounts to tempt
customer)
27. Collaborative
recommendation
RangeelaQSQT 100 daysAnand Sholay Deewar Vertigo
Smita
Vijay
Mohan
Rajesh
Nina
Nitin ? ? ? ? ? ?
•Possible approaches:
• Average vote along columns [Same prediction for all]
• Weight vote based on similarity of likings [GroupLens]
RangeelaQSQT 100 daysAnand Sholay Deewar Vertigo
Smita
Vijay
Mohan
Rajesh
Nina
Nitin ? ? ? ? ? ?
28. Cluster-based approaches
External attributes of people and movies to
cluster
age, gender of people
actors and directors of movies.
[ May not be available]
Cluster people based on movie preferences
misses information about similarity of movies
Repeated clustering:
cluster movies based on people, then people based on
movies, and repeat
ad hoc, might smear out groups
30. Model-based approach
People and movies belong to unknown classes
Pk = probability a random person is in class k
Pl = probability a random movie is in class l
Pkl = probability of a class-k person liking a
class-l movie
Gibbs sampling: iterate
Pick a person or movie at random and assign to a
class with probability proportional to Pk or Pl
Estimate new parameters
Need statistics background to understand details
32. Association rules
Given set T of groups of items
Example: set of item sets purchased
Goal: find all rules on itemsets of the
form a-->b such that
support of a and b > user threshold s
conditional probability (confidence) of b
given a > user threshold c
Example: Milk --> bread
Purchase of product A --> service B
Milk, cereal
Tea, milk
Tea, rice, bread
cereal
T
33. Variants
High confidence may not imply high
correlation
Use correlations. Find expected support
and large departures from that
interesting..
see statistical literature on contingency tables.
Still too many rules, need to prune...
34. Prevalent Interesting
Analysts already
know about prevalent
rules
Interesting rules are
those that deviate
from prior
expectation
Mining’s payoff is in
finding surprising
phenomena
1995
1998
Milk and
cereal sell
together!
Zzzz... Milk and
cereal sell
together!
35. What makes a rule
surprising?
Does not match prior
expectation
Correlation between
milk and cereal remains
roughly constant over
time
Cannot be trivially
derived from simpler
rules
Milk 10%, cereal 10%
Milk and cereal 10% …
surprising
Eggs 10%
Milk, cereal and eggs
0.1% … surprising!
Expected 1%
36. Applications of fast
itemset counting
Find correlated events:
Applications in medicine: find redundant
tests
Cross selling in retail, banking
Improve predictive capability of classifiers
that assume attribute independence
New similarity measures of categorical
attributes [Mannila et al, KDD 98]
38. Application Areas
Industry Application
Finance Credit Card Analysis
Insurance Claims, Fraud Analysis
Telecommunication Call record analysis
Transport Logistics management
Consumer goods promotion analysis
Data Service providers Value added data
Utilities Power usage analysis
39. Why Now?
Data is being produced
Data is being warehoused
The computing power is available
The computing power is affordable
The competitive pressures are strong
Commercial products are available
40. Data Mining works with
Warehouse Data
Data Warehousing provides the
Enterprise with a memory
ÑData Mining provides the
Enterprise with intelligence
41. Usage scenarios
Data warehouse mining:
assimilate data from operational sources
mine static data
Mining log data
Continuous mining: example in process control
Stages in mining:
data selection pre-processing: cleaning
transformation mining result
evaluation visualization
42. Mining market
Around 20 to 30 mining tool vendors
Major tool players:
Clementine,
IBM’s Intelligent Miner,
SGI’s MineSet,
SAS’s Enterprise Miner.
All pretty much the same set of tools
Many embedded products:
fraud detection:
electronic commerce applications,
health care,
customer relationship management: Epiphany
43. Vertical integration:
Mining on the web
Web log analysis for site design:
what are popular pages,
what links are hard to find.
Electronic stores sales enhancements:
recommendations, advertisement:
Collaborative filtering: Net perception, Wisewire
Inventory control: what was a shopper
looking for and could not find..
44. OLAP Mining integration
OLAP (On Line Analytical Processing)
Fast interactive exploration of multidim.
aggregates.
Heavy reliance on manual operations for
analysis:
Tedious and error-prone on large
multidimensional data
Ideal platform for vertical integration of mining
but needs to be interactive instead of batch.
45. State of art in mining OLAP
integration
Decision trees [Information discovery, Cognos]
find factors influencing high profits
Clustering [Pilot software]
segment customers to define hierarchy on that dimension
Time series analysis: [Seagate’s Holos]
Query for various shapes along time: eg. spikes, outliers
Multi-level Associations [Han et al.]
find association between members of dimensions
Sarawagi [VLDB2000]
46. Data Mining in Use
The US Government uses Data Mining to track
fraud
A Supermarket becomes an information broker
Basketball teams use it to track game strategy
Cross Selling
Target Marketing
Holding on to Good Customers
Weeding out Bad Customers
47. Some success stories
Network intrusion detection using a combination of
sequential rule discovery and classification tree on 4 GB
DARPA data
Won over (manual) knowledge engineering approach
http://www.cs.columbia.edu/~sal/JAM/PROJECT/ provides good
detailed description of the entire process
Major US bank: customer attrition prediction
First segment customers based on financial behavior: found 3
segments
Build attrition models for each of the 3 segments
40-50% of attritions were predicted == factor of 18 increase
Targeted credit marketing: major US banks
find customer segments based on 13 months credit balances
build another response model based on surveys
increased response 4 times -- 2%
Editor's Notes
Any area where large amounts of historic data that if understood
better can help shape future decisions.
Each topic is a talk..
Absolute: 40 M$
40M$, expected to grow 10 times by 2000 --Forrester research
OLAP refers to Online Analytical Processing.
I always wondered what was the analytical part in olap products?
OLAP -- bunch of aggregates and simple group-bys on sums and average is not analysis. Interactive speed for selects/drill-downs/rollups/ no joins “analysis” manually and the products meet the “Online” part of the promise by pre-computing the aggregates.
They offer a bare-bones RISC like functionality using which
analysts do most of the work manually.
This talk is about investigating if we can do some of the analysis too?
When you have 5 dimensions, with avg. 3 levels hierarchy on each
aggregating more than a million rows, manual exploration can get
tedious.
Goal is to add more complex operations although called mining
think of them more like CISC functionalities..
Mining products provide the analysis part but they do it
batched rather than online. Greater success of OLAP means people find this form of interactive analysis quite attractive.
Littl e integration: here are few exceptions ---
People are starting to wake up to this possibility and here are some examples I have found by web-surfing.
. decision tree most common. Information Discovery claimed to be only serious integrator [DBMS Ap ‘98]
Clustering used by some to define new product hierarchies.
Of course, rich set of time-series functions especially for forecasting was always there
New charting software: 80/20, A-B-C analysis, quadrant plotting.
Univ. Jiawen Han.
Previous approach has been to bring in mining operations in olap. Look at mining operations and choose what fits.
My approach has been to reflect on what people do with cube metaphor
and the drill-down, roll-up, based exploration and see if there is anything there that can be automated.
Discuss my work first.