Data Mining and Data Visualization

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Data Mining and Data Visualization
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Codds Twelve Rules for OLAP

• Multidimensional view
• Transparent to the user
• Accessible
• Consistent reporting
• Client-server architecture
• Generic dimensionality
• Dynamic sparse matrix handling
• Multi-user support
• Cross-dimensional operations
• Intuitive data manipulation
• Flexible reporting
• Unlimited levels of dimension and aggregation

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Multidimensional Analysis of Data
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Typical Star Schema Data Organization
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General Process of Knowledge Discovery

• Select a topic for study develop an
  understanding of the business problem.
• Identify a target data set(s) determine what
  data is relevant for study.
• Clean and preprocess the data identify missing
  data fields, data noise, etc.
• Build a data model use the mining software to
  construct a mathematical model explaining the
  varying impacts of the inputs on the outputs.
• Mine the data search for patterns of interest
  (not all patterns identified by the model will be
  interesting).
• Interpret and refine review the results of the
  initial mining and use the results to refine the
  model, as necessary, to gain additional insight
  into identified patterns of interest.
• Predict use the refined model to predict the
  output for a set of inputs where the output is
  not yet known.
• Share the model once validated, make the model
  available to other warehouse users

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Data Visualization of Global Private Computer
Network Activity
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Natural Gas Pipeline Analysis
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Risk Analysis Report
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Interactive Telephone Polling Discovery
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Typical Applications of Data Visualization
Techniques

• Retail Banking
• customer/product cross-selling analysis
• electronic banking management
• credit risk
• product pricing strategy
• Government
• budget analysis
• resource management
• economic analysis
• fraud detection and drug trafficking analysis
• Insurance
• asset/liability management
• actuarial modeling
• workflow management and analysis
• Health Care and Medicine
• claims analysis

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Typical Applications of Data Visualization
Techniques

• Telecommunications
• call-center management
• network operations management
• service policy analysis
• market analysis
• loading pattern analysis
• Transportation
• yield analysis
• asset utilization
• fleet management
• Capital Markets
• risk assessment
• derivatives trading
• institutional sales systems
• retail marketing
• Asset Management