CS490D: Introduction to Data Mining Prof. Chris CliftonHow to Choose a Data Mining System?How to Choose a Data Mining System? (2)How to Choose a Data Mining System? (3)Examples of Data Mining Systems (1)Examples of Data Mining Systems (2)Slide 7SIGMOD’04 ScholarshipsSlide 9Data Mining ProcessWhy Should There be a Standard Process?Process StandardizationCRISP-DMCRISP-DM: OverviewCRISP-DM: PhasesPhases and TasksPhases in the DM Process (1 & 2)Phases in the DM Process (3)Phases in the DM Process (4)Phases in the DM Process (5)Phases in the DM Process (6)Why CRISP-DM?Slide 31Attribute-Oriented InductionBasic Principles of Attribute-Oriented InductionAttribute-Oriented Induction: Basic AlgorithmExampleClass Characterization: An ExamplePresentation of Generalized ResultsPresentation—Generalized RelationPresentation—CrosstabSlide 41Characterization vs. OLAPAttribute Relevance AnalysisAttribute relevance analysis (cont’d)Relevance MeasuresInformation-Theoretic ApproachExample: Analytical CharacterizationExample: Analytical Characterization (cont’d)Example: Analytical characterization (2)Example: Analytical characterization (3)Example: Analytical Characterization (4)Example: Analytical characterization (5)Slide 55Mining Class ComparisonsExample: Analytical comparisonExample: Analytical comparison (2)Example: Analytical comparison (3)Example: Analytical comparison (4)Example: Analytical comparison (5)Quantitative Discriminant RulesExample: Quantitative Discriminant RuleClass DescriptionExample: Quantitative Description RuleSlide 75Mining Data Dispersion CharacteristicsHistogram AnalysisQuantile PlotQuantile-Quantile (Q-Q) PlotScatter plotLoess CurveSummaryReferencesReferences (cont.)CS490D:Introduction to Data MiningProf. Chris CliftonMarch 12, 2004Data Mining ProcessCS490D 2How to Choose a Data Mining System?•Commercial data mining systems have little in common –Different data mining functionality or methodology –May even work with completely different kinds of data sets•Need multiple dimensional view in selection•Data types: relational, transactional, text, time sequence, spatial?•System issues–running on only one or on several operating systems?–a client/server architecture?–Provide Web-based interfaces and allow XML data as input and/or output?CS490D 3How to Choose a Data Mining System? (2)•Data sources–ASCII text files, multiple relational data sources–support ODBC connections (OLE DB, JDBC)?•Data mining functions and methodologies–One vs. multiple data mining functions–One vs. variety of methods per function•More data mining functions and methods per function provide the user with greater flexibility and analysis power•Coupling with DB and/or data warehouse systems–Four forms of coupling: no coupling, loose coupling, semitight coupling, and tight coupling•Ideally, a data mining system should be tightly coupled with a database systemCS490D 4How to Choose a Data Mining System? (3)•Scalability–Row (or database size) scalability–Column (or dimension) scalability–Curse of dimensionality: it is much more challenging to make a system column scalable that row scalable•Visualization tools–“A picture is worth a thousand words”–Visualization categories: data visualization, mining result visualization, mining process visualization, and visual data mining•Data mining query language and graphical user interface–Easy-to-use and high-quality graphical user interface –Essential for user-guided, highly interactive data miningCS490D 5Examples 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 standardCS490D 6Examples 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 warehousesCS490D:Introduction to Data MiningProf. Chris CliftonMarch 22, 2004CRISP-DMThanks to Laura Squier, SPSS for some of the material usedCS490D 8SIGMOD’04 Scholarships•Want to learn more about Database and Data Mining Research?–SIGMOD is the premier database research conference•Want $1000 off a trip to France this summer?–June 13-18, Paris•Application Deadline March 26–Details: http://www.cs.rpi.edu/sigmod-ugradCS490D:Introduction to Data MiningProf. Chris CliftonMarch 22, 2004CRISP-DMThanks to Laura Squier, SPSS for some of the material usedCS490D 10Data Mining Process•Cross-Industry Standard Process for Data Mining (CRISP-DM)•European Community funded effort to develop framework for data mining tasks•Goals:–Encourage interoperable tools across entire data mining process–Take the mystery/high-priced expertise out of simple data mining tasksCS490D 11Why Should There be a Standard Process?•Framework for recording experience–Allows projects to be replicated•Aid to project planning and management•“Comfort factor” for new adopters–Demonstrates maturity of Data Mining–Reduces dependency on “stars”The data mining process must The data mining process must be reliable and repeatable by be reliable and repeatable by people with little data mining people with little data mining background.background.CS490D 12Process Standardization•CRoss Industry Standard Process for Data Mining•Initiative launched Sept.1996•SPSS/ISL, NCR, Daimler-Benz, OHRA•Funding from European commission•Over 200 members of the CRISP-DM SIG worldwide–DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, Syllogic, Magnify, ..–System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte & Touche, …–End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ...CS490D