Data Warehousing and Mining

  • type: practical course
  • chair: 0EA9AC5C234342C49BF428CC80B9399C
  • semester: 8
  • place: Seminarraum 348
  • time: nach Vereinbarung
  • start: nach Vereinbarung
  • lecturer:

    Prof. K. Böhm
    F. Eichinger
    S. Schosser

  • sws: 2
  • ects: 4
  • lv-no.: 24874


The practical course data warehousing and mining will deepen the theoretical knowledge from the lecture "Data Warehousing and Mining", with a focus on practical aspects and common tools. The course is divided into two blocks, data warehousing and data mining. The data warehousing block focuses on data preprocessing and building data warehouses. The data-mining block roughly follows the KDD process with practical knowledge-discovery examples in businesses. With such examples, the different data-mining concepts are investigated. The focus is on techniques for clustering, classification and discovering frequent itemsets and association rules. Working in teams is another important aspect in the whole course.


  • J. Han und M. Kamber: "Data Mining: Concepts and Techniques", Morgan Kaufmann, 2006.
  • I. H. Witten und E. Frank: "Data Mining - Practical Machine Learning Tools and Techniques", Morgan Kaufmann, 2005.
  • D. Hand, H. Mannila und P. Smyth: "Principles of Data Mining", MIT Press, 2001.
  • L. I. Kuncheva: "Combining Pattern Classifiers", Wiley-Interscience, 2004.
  • A. Bauer, H. Günzel: "Data Warehouse Systeme – Architektur, Entwicklung, Anwendung", dpunkt.verlag, 2004.