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Overview

Workload: SWS : 4, Credits 8

Participants: Expected: 60

Course type : Lecture

Language: english

Examination date: tba

Materials

Content

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

  • Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F., "Guide to intelligent data analysis: How to intelligently make sense of real data", Springer, 2010
  • 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 Assessment

 there will be programming exercises and theoretical exercises which are a requirement for taking the exam. The final grade will only be based on the oral exam at the end of the semester. 20 minutes.

Prerequisites

Basic mathematical skills(statistics), basic knowledge in programming (JAVA), basic courses in computer science.