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Post Reply Close Saving..... Edit Comment Close Premium member Presentation Transcript Data Mining: Trends and Applications : June 7, 2010 Data Mining: Concepts and Techniques 1 Data Mining: Trends and Applications ©Jiawei Han and Micheline Kamber Babu Ram Dawadi Data Mining?? : Data Mining?? June 7, 2010 Data Mining: Concepts and Techniques 2 Data Mining: The process of Discovering meaningful patterns & trends often previously unknown, by shifting large amount of data, using pattern recognition, statistical and Mathematical techniques. A group of techniques that find relationship that have not previously been discovered What Is Data Mining? : CH#2, Data Warehousing By: Babu Ram Dawadi What Is Data Mining? Data mining (knowledge discovery in databases): Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names and their “inside stories”: Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. What is not data mining? (Deductive) query processing. Expert systems Data Mining: Confluence of Multiple Disciplines : June 7, 2010 Data Mining: Concepts and Techniques 4 Data Mining: Confluence of Multiple Disciplines Data Mining: On What Kinds of Data? : June 7, 2010 Data Mining: Concepts and Techniques 5 Data Mining: On What Kinds of Data? Database-oriented data sets and applications Relational database, data warehouse, transactional database Advanced data sets and advanced applications Data streams and sensor data Time-series data, temporal data, sequence data (incl. bio-sequences) Structure data, graphs, social networks and multi-linked data Object-relational databases Heterogeneous databases and legacy databases Spatial data and spatiotemporal data Multimedia database Text databases The World-Wide Web Data Mining Applications : June 7, 2010 Data Mining: Concepts and Techniques 6 Data Mining Applications Data mining is a young discipline with wide and diverse applications There is still a nontrivial gap between general principles of data mining and domain-specific, effective data mining tools for particular applications Some application domains Biomedical and DNA data analysis Financial data analysis Retail industry Telecommunication industry Biomedical Data Mining and DNA Analysis : June 7, 2010 Data Mining: Concepts and Techniques 7 Biomedical Data Mining and DNA Analysis DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T). Gene: a sequence of hundreds of individual nucleotides arranged in a particular order Humans have around 100,000 genes Tremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genes Semantic integration of heterogeneous, distributed genome databases Current: highly distributed, uncontrolled generation and use of a wide variety of DNA data Data cleaning and data integration methods developed in data mining will help DNA Analysis: Examples : June 7, 2010 Data Mining: Concepts and Techniques 8 DNA Analysis: Examples Similarity search and comparison among DNA sequences Compare the frequently occurring patterns of each class (e.g., diseased and healthy) Identify gene sequence patterns that play roles in various diseases Association analysis: identification of co-occurring gene sequences Most diseases are not triggered by a single gene but by a combination of genes acting together Association analysis may help determine the kinds of genes that are likely to co-occur together in target samples Path analysis: linking genes to different disease development stages Different genes may become active at different stages of the disease Develop pharmaceutical interventions that target the different stages separately Data Mining for Financial Data Analysis : June 7, 2010 Data Mining: Concepts and Techniques 9 Data Mining for Financial Data Analysis Loan payment prediction/consumer credit policy analysis feature selection and attribute relevance ranking Loan payment performance Consumer credit rating Classification and clustering of customers for targeted marketing multidimensional segmentation by nearest-neighbor, classification, decision Detection of money laundering and other financial crimes Tools: data visualization, linkage analysis, classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences) Data Mining for Retail Industry : June 7, 2010 Data Mining: Concepts and Techniques 10 Data Mining for Retail Industry Retail industry: huge amounts of data on sales, customer shopping history, etc. Applications of retail data mining Identify customer buying behaviors Discover customer shopping patterns and trends Improve the quality of customer service Achieve better customer retention and satisfaction Enhance goods consumption ratios Design more effective goods transportation and distribution policies Data Mining for Telecomm. Industry (1) : June 7, 2010 Data Mining: Concepts and Techniques 11 Data Mining for Telecomm. Industry (1) A rapidly expanding and highly competitive industry and a great demand for data mining Understand the business involved Identify telecommunication patterns Catch fraudulent activities Make better use of resources Improve the quality of service Multidimensional analysis of telecommunication data Intrinsically multidimensional: calling-time, duration, location of caller, location of callee, type of call, etc. Data Mining for Telecomm. Industry (2) : June 7, 2010 Data Mining: Concepts and Techniques 12 Data Mining for Telecomm. Industry (2) Fraudulent pattern analysis and the identification of unusual patterns Identify potentially fraudulent users and their atypical usage patterns Detect attempts to gain fraudulent entry to customer accounts Discover unusual patterns which may need special attention Multidimensional association and sequential pattern analysis Find usage patterns for a set of communication services by customer group, by month, etc. Promote the sales of specific services Improve the availability of particular services in a region Corporate Analysis & Risk Management : June 7, 2010 Data Mining: Concepts and Techniques 13 Corporate Analysis & Risk Management Finance planning and asset evaluation cash flow analysis and prediction claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning summarize and compare the resources and spending Competition monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in a highly competitive market Fraud Detection & Mining Unusual Patterns : June 7, 2010 Data Mining: Concepts and Techniques 14 Fraud Detection & Mining Unusual Patterns Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. Money laundering: suspicious monetary transactions Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Retail industry Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism Examples of Data Mining Systems (1) : June 7, 2010 Data Mining: Concepts and Techniques 15 Examples of Data Mining Systems (1) IBM Intelligent Miner A wide range of data mining algorithms Scalable mining algorithms Toolkits: neural network algorithms, statistical methods, data preparation, and data visualization tools Tight integration with IBM's DB2 relational database system SAS Enterprise Miner A variety of statistical analysis tools Data warehouse tools and multiple data mining algorithms Mirosoft SQLServer 2000 Integrate DB and OLAP with mining Support OLEDB for DM standard Examples of Data Mining Systems (2) : June 7, 2010 Data Mining: Concepts and Techniques 16 Examples of Data Mining Systems (2) SGI MineSet Multiple data mining algorithms and advanced statistics Advanced visualization tools Clementine (SPSS) An integrated data mining development environment for end-users and developers Multiple data mining algorithms and visualization tools DBMiner (DBMiner Technology Inc.) Multiple data mining modules: discovery-driven OLAP analysis, association, classification, and clustering Efficient, association and sequential-pattern mining functions, and visual classification tool Mining both relational databases and data warehouses Data Mining and Intelligent Query Answering : June 7, 2010 Data Mining: Concepts and Techniques 17 Data Mining and Intelligent Query Answering Query answering Direct query answering: returns exactly what is being asked Intelligent (or cooperative) query answering: analyzes the intent of the query and provides generalized, neighborhood or associated information relevant to the query Some users may not have a clear idea of exactly what to mine or what is contained in the database Intelligent query answering analyzes the user's intent and answers queries in an intelligent way Data Mining and Intelligent Query Answering (2) : June 7, 2010 Data Mining: Concepts and Techniques 18 Data Mining and Intelligent Query Answering (2) A general framework for the integration of data mining and intelligent query answering Data query: finds concrete data stored in a database Knowledge query: finds rules, patterns, and other kinds of knowledge in a database Ex. Three ways to improve on-line shopping service Informative query answering by providing summary information Suggestion of additional items based on association analysis Product promotion by sequential pattern mining Data Mining: Merely Managers' Business or Everyone's? : June 7, 2010 Data Mining: Concepts and Techniques 19 Data Mining: Merely Managers' Business or Everyone's? Data mining will surely be an important tool for managers’ decision making Bill Gates: “Business @ the speed of thought” The amount of the available data is increasing, and data mining systems will be more affordable Multiple personal uses Mine your family's medical history to identify genetically-related medical conditions Mine the records of the companies you deal with Mine data on stocks and company performance, etc. Invisible data mining Build data mining functions into many intelligent tools Trends in Data Mining (1) : June 7, 2010 Data Mining: Concepts and Techniques 20 Trends in Data Mining (1) Application exploration development of application-specific data mining system Invisible data mining (mining as built-in function) Scalable data mining methods Constraint-based mining: use of constraints to guide data mining systems in their search for interesting patterns Integration of data mining with database systems, data warehouse systems, and Web database systems Summary : June 7, 2010 Data Mining: Concepts and Techniques 21 Summary Domain-specific applications include biomedicine (DNA), finance, retail and telecommunication data mining There exist some data mining systems and it is important to know their power and limitations Intelligent query answering can be integrated with mining You do not have the permission to view this presentation. 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Chapter1_applicationDM baburd Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 336 Category: Science & Tech.. License: All Rights Reserved Like it (1) Dislike it (0) Added: June 07, 2010 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... By: cavi (16 month(s) ago) sir, i have seen ur presentation and i liked it much so please mail it to my email id:"chinataavinash@gmail.com" plzzzzzzzzz.. Saving..... Post Reply Close Saving..... Edit Comment Close By: shilpalaggyshetty (16 month(s) ago) please can u mail this ppt...i need this info Saving..... Post Reply Close Saving..... Edit Comment Close Premium member Presentation Transcript Data Mining: Trends and Applications : June 7, 2010 Data Mining: Concepts and Techniques 1 Data Mining: Trends and Applications ©Jiawei Han and Micheline Kamber Babu Ram Dawadi Data Mining?? : Data Mining?? June 7, 2010 Data Mining: Concepts and Techniques 2 Data Mining: The process of Discovering meaningful patterns & trends often previously unknown, by shifting large amount of data, using pattern recognition, statistical and Mathematical techniques. A group of techniques that find relationship that have not previously been discovered What Is Data Mining? : CH#2, Data Warehousing By: Babu Ram Dawadi What Is Data Mining? Data mining (knowledge discovery in databases): Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names and their “inside stories”: Knowledge discovery(mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. What is not data mining? (Deductive) query processing. Expert systems Data Mining: Confluence of Multiple Disciplines : June 7, 2010 Data Mining: Concepts and Techniques 4 Data Mining: Confluence of Multiple Disciplines Data Mining: On What Kinds of Data? : June 7, 2010 Data Mining: Concepts and Techniques 5 Data Mining: On What Kinds of Data? Database-oriented data sets and applications Relational database, data warehouse, transactional database Advanced data sets and advanced applications Data streams and sensor data Time-series data, temporal data, sequence data (incl. bio-sequences) Structure data, graphs, social networks and multi-linked data Object-relational databases Heterogeneous databases and legacy databases Spatial data and spatiotemporal data Multimedia database Text databases The World-Wide Web Data Mining Applications : June 7, 2010 Data Mining: Concepts and Techniques 6 Data Mining Applications Data mining is a young discipline with wide and diverse applications There is still a nontrivial gap between general principles of data mining and domain-specific, effective data mining tools for particular applications Some application domains Biomedical and DNA data analysis Financial data analysis Retail industry Telecommunication industry Biomedical Data Mining and DNA Analysis : June 7, 2010 Data Mining: Concepts and Techniques 7 Biomedical Data Mining and DNA Analysis DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T). Gene: a sequence of hundreds of individual nucleotides arranged in a particular order Humans have around 100,000 genes Tremendous number of ways that the nucleotides can be ordered and sequenced to form distinct genes Semantic integration of heterogeneous, distributed genome databases Current: highly distributed, uncontrolled generation and use of a wide variety of DNA data Data cleaning and data integration methods developed in data mining will help DNA Analysis: Examples : June 7, 2010 Data Mining: Concepts and Techniques 8 DNA Analysis: Examples Similarity search and comparison among DNA sequences Compare the frequently occurring patterns of each class (e.g., diseased and healthy) Identify gene sequence patterns that play roles in various diseases Association analysis: identification of co-occurring gene sequences Most diseases are not triggered by a single gene but by a combination of genes acting together Association analysis may help determine the kinds of genes that are likely to co-occur together in target samples Path analysis: linking genes to different disease development stages Different genes may become active at different stages of the disease Develop pharmaceutical interventions that target the different stages separately Data Mining for Financial Data Analysis : June 7, 2010 Data Mining: Concepts and Techniques 9 Data Mining for Financial Data Analysis Loan payment prediction/consumer credit policy analysis feature selection and attribute relevance ranking Loan payment performance Consumer credit rating Classification and clustering of customers for targeted marketing multidimensional segmentation by nearest-neighbor, classification, decision Detection of money laundering and other financial crimes Tools: data visualization, linkage analysis, classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences) Data Mining for Retail Industry : June 7, 2010 Data Mining: Concepts and Techniques 10 Data Mining for Retail Industry Retail industry: huge amounts of data on sales, customer shopping history, etc. Applications of retail data mining Identify customer buying behaviors Discover customer shopping patterns and trends Improve the quality of customer service Achieve better customer retention and satisfaction Enhance goods consumption ratios Design more effective goods transportation and distribution policies Data Mining for Telecomm. Industry (1) : June 7, 2010 Data Mining: Concepts and Techniques 11 Data Mining for Telecomm. Industry (1) A rapidly expanding and highly competitive industry and a great demand for data mining Understand the business involved Identify telecommunication patterns Catch fraudulent activities Make better use of resources Improve the quality of service Multidimensional analysis of telecommunication data Intrinsically multidimensional: calling-time, duration, location of caller, location of callee, type of call, etc. Data Mining for Telecomm. Industry (2) : June 7, 2010 Data Mining: Concepts and Techniques 12 Data Mining for Telecomm. Industry (2) Fraudulent pattern analysis and the identification of unusual patterns Identify potentially fraudulent users and their atypical usage patterns Detect attempts to gain fraudulent entry to customer accounts Discover unusual patterns which may need special attention Multidimensional association and sequential pattern analysis Find usage patterns for a set of communication services by customer group, by month, etc. Promote the sales of specific services Improve the availability of particular services in a region Corporate Analysis & Risk Management : June 7, 2010 Data Mining: Concepts and Techniques 13 Corporate Analysis & Risk Management Finance planning and asset evaluation cash flow analysis and prediction claim analysis to evaluate assets cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning summarize and compare the resources and spending Competition monitor competitors and market directions group customers into classes and a class-based pricing procedure set pricing strategy in a highly competitive market Fraud Detection & Mining Unusual Patterns : June 7, 2010 Data Mining: Concepts and Techniques 14 Fraud Detection & Mining Unusual Patterns Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. Money laundering: suspicious monetary transactions Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm Retail industry Analysts estimate that 38% of retail shrink is due to dishonest employees Anti-terrorism Examples of Data Mining Systems (1) : June 7, 2010 Data Mining: Concepts and Techniques 15 Examples of Data Mining Systems (1) IBM Intelligent Miner A wide range of data mining algorithms Scalable mining algorithms Toolkits: neural network algorithms, statistical methods, data preparation, and data visualization tools Tight integration with IBM's DB2 relational database system SAS Enterprise Miner A variety of statistical analysis tools Data warehouse tools and multiple data mining algorithms Mirosoft SQLServer 2000 Integrate DB and OLAP with mining Support OLEDB for DM standard Examples of Data Mining Systems (2) : June 7, 2010 Data Mining: Concepts and Techniques 16 Examples of Data Mining Systems (2) SGI MineSet Multiple data mining algorithms and advanced statistics Advanced visualization tools Clementine (SPSS) An integrated data mining development environment for end-users and developers Multiple data mining algorithms and visualization tools DBMiner (DBMiner Technology Inc.) Multiple data mining modules: discovery-driven OLAP analysis, association, classification, and clustering Efficient, association and sequential-pattern mining functions, and visual classification tool Mining both relational databases and data warehouses Data Mining and Intelligent Query Answering : June 7, 2010 Data Mining: Concepts and Techniques 17 Data Mining and Intelligent Query Answering Query answering Direct query answering: returns exactly what is being asked Intelligent (or cooperative) query answering: analyzes the intent of the query and provides generalized, neighborhood or associated information relevant to the query Some users may not have a clear idea of exactly what to mine or what is contained in the database Intelligent query answering analyzes the user's intent and answers queries in an intelligent way Data Mining and Intelligent Query Answering (2) : June 7, 2010 Data Mining: Concepts and Techniques 18 Data Mining and Intelligent Query Answering (2) A general framework for the integration of data mining and intelligent query answering Data query: finds concrete data stored in a database Knowledge query: finds rules, patterns, and other kinds of knowledge in a database Ex. Three ways to improve on-line shopping service Informative query answering by providing summary information Suggestion of additional items based on association analysis Product promotion by sequential pattern mining Data Mining: Merely Managers' Business or Everyone's? : June 7, 2010 Data Mining: Concepts and Techniques 19 Data Mining: Merely Managers' Business or Everyone's? Data mining will surely be an important tool for managers’ decision making Bill Gates: “Business @ the speed of thought” The amount of the available data is increasing, and data mining systems will be more affordable Multiple personal uses Mine your family's medical history to identify genetically-related medical conditions Mine the records of the companies you deal with Mine data on stocks and company performance, etc. Invisible data mining Build data mining functions into many intelligent tools Trends in Data Mining (1) : June 7, 2010 Data Mining: Concepts and Techniques 20 Trends in Data Mining (1) Application exploration development of application-specific data mining system Invisible data mining (mining as built-in function) Scalable data mining methods Constraint-based mining: use of constraints to guide data mining systems in their search for interesting patterns Integration of data mining with database systems, data warehouse systems, and Web database systems Summary : June 7, 2010 Data Mining: Concepts and Techniques 21 Summary Domain-specific applications include biomedicine (DNA), finance, retail and telecommunication data mining There exist some data mining systems and it is important to know their power and limitations Intelligent query answering can be integrated with mining