CSEN 911

2 lecture hours
2 exercise hours

4  ECTS credits

Data Mining

Abstract

  • Recently there has been an increasing interest in research and practice of data mining. Data mining is about finding hidden patterns and unknown facts in large data sets. Researchers use various techniques to mine data that belong to various background disciplines. This includes, but not limited to: Regression, Correlation, Association rules, Cluster analysis, and many others. In terms of data mining results, decisions makers and analysts used the mining output in both directions: description and prediction. This course introduces those concepts in detail.

Outline

  • Topics

    The course will have hands-on experience with two well-known data mining tools: Weka and Teradata Miner. Students will have a chance to study the tools and work on sample datasets. When time permits students will be able to customize the package.

    • Introduction to Data Mining
    • Data mining and knowledge discovery in database (KDD)
    • Data Mining tasks
    • Data warehousing: is it necessary for the mining process?
    • Data Mining techniques and algorithms
      • SQL/ DMQL
      • OLAP
      • Regression and Correlation
      • Association Rules
      • Cluster analysis
      • Others
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