1CS490D: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?2CS490D 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, semitightcoupling, 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 mining3CS490D 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 warehouses4CS490D:Introduction to Data MiningProf. Chris CliftonMarch 22, 2004CRISP-DMThanks to Laura Squier, SPSS for some of the material usedCS490D 8Data 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 tasks5CS490D 9Why 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 10Process 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, ...6CS490D 11CRISP-DM• Non-proprietary• Application/Industry neutral• Tool neutral• Focus on business issues– As well as technical analysis• Framework for guidance• Experience base– Templates for AnalysisCS490D 12CRISP-DM: Overview7CS490D 13CRISP-DM: Phases• Business Understanding– Understanding project objectives and requirements– Data mining problem definition• Data Understanding– Initial data collection and familiarization– Identify data quality issues– Initial, obvious results• Data Preparation– Record and attribute selection– Data cleansing• Modeling– Run the data mining tools• Evaluation– Determine if results meet business objectives– Identify business issues that should have been addressed earlier• Deployment– Put the resulting models into practice– Set up for repeated/continuous mining of the dataCS490D 14BusinessUnderstandingDataUnderstandingEvaluationDataPreparationModelingDetermine Business ObjectivesBackgroundBusiness ObjectivesBusiness Success CriteriaSituation AssessmentInventory of ResourcesRequirements,Assumptions, andConstraintsRisks and ContingenciesTerminologyCosts and BenefitsDetermine Data Mining GoalData Mining GoalsData Mining Success CriteriaProduce Project PlanProject PlanInitial Asessment of Tools and TechniquesCollect Initial DataInitial Data Collection ReportDescribe DataData Description ReportExplore DataData Exploration Report Verify Data Quality Data Quality ReportData SetData Set DescriptionSelect Data Rationale for Inclusion / ExclusionClean Data Data Cleaning ReportConstruct DataDerived AttributesGenerated RecordsIntegrate DataMerged DataFormat DataReformatted DataSelect ModelingTechniqueModeling TechniqueModeling AssumptionsGenerate Test DesignTest DesignBuild ModelParameter SettingsModelsModel DescriptionAssess ModelModel AssessmentRevised Parameter SettingsEvaluate ResultsAssessment of Data Mining Results w.r.t. Business Success CriteriaApproved ModelsReview ProcessReview of ProcessDetermine Next StepsList of Possible ActionsDecisionPlan DeploymentDeployment