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Overview

Leistung: SWS : 5, Credits 8

Course type:  Lecture (Bachelor/Master)

Language: english

Materials

all materials will be provided in ILIAS

Content

Link to LSF

The lecture series provides an introduction to Data Mining Methods with an emphasis placed on basic approaches and how they are incorporated into different problem definitions.

  • Data Mining: problem definition, motivation, application examples
  • Modelling: data-driven concept development, presentation of hypotheses
  • Version space and the evaluation of hypotheses
  • Clustering methods
  • Regression
  • Association rules

Literature

Han J., Kamber M., „Data Mining: Concepts and Techniques“, Morgan Kaufmann Publishers, August 2000.

Ester M., Sander J., „Knowledge Discovery in Databases. Techniken und Anwendungen“, Springer, 2000.

Hand D.J., Mannila H., Smyth P., „Principles of Data Mining“, MIT Press, 2001.

Mitchell T. M., „Machine Learning“, McGraw-Hill, 1997.

Witten I. H., Frank E., „Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations“, Morgan Kaufmann Publishers, 2000.

Course criteria

  • 50% of the sum of all points on the exercise sheets
  • active participation in the exercise
  • Written or oral exam at the end of the semester

Prerequisites

none