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Multi-Dimensional View of Data Mining

  • Data to be mined
    • Database data (extended-relational, object-oriented, heterogeneous, legacy), data warehouse, transactional data, stream, spatiotemporal, time-series, sequence, text and web, multi-media, graphs & social and information networks
  • Knowledge to be mined (or: Data mining functions)
    • Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc.
    • Descriptive vs. predictive data mining
    • Multiple/integrated functions and mining at multiple levels
  • Techniques utilized
    • Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition, visualization, high-performance, etc.
  • Applications adapted
    • Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc.

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