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Unformatted text preview: Data Preparation Modeling Evaluation Deployment Determine Business Objectives Background Business Objectives Business Success Criteria Select Modeling Evaluate Results Technique Assessment of Data Modeling Technique Mining Results w.r.t. Select Data Modeling Assumptions Business Success Describe Data Rationale for Inclusion / Criteria Data Description Report Exclusion Generate Test Design Approved Models Test Design Situation Assessment Explore Data Clean Data Review Process Inventory of Resources Data Exploration Report Data Cleaning Report Build Model Review of Process Requirements, Parameter Settings Assumptions, and Verify Data Quality Construct Data Models Determine Next Steps Constraints Data Quality Report Derived Attributes Model Description List of Possible Actions Risks and Contingencies Generated Records Decision Terminology Assess Model Costs and Benefits Integrate Data Model Assessment Merged Data Revised Parameter Determine Settings Data Mining Goal Format Data Data Mining Goals Reformatted Data Data Mining Success Criteria Produce Project Plan Project Plan Initial Asessment of Tools and Techniques Collect Initial Data Initial Data Collection Report Data Set Data Set Description Plan Deployment Deployment Plan Plan Monitoring and Maintenance Monitoring and Maintenance Plan Produce Final Report Final Report Final Presentation Review Project Experience Documentation Source: SPSS BI Model Building
Predictive or Descriptive Selecting data mining tools Transforming data if needed Generating samples (as necessary) for training, testing and validating the model Build, test and select models. C...
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This note was uploaded on 09/17/2009 for the course IT it771 taught by Professor Jenisha during the Fall '09 term at University of Advancing Technology.
- Fall '09
- Data Mining