Login |


Leistung: SWS : 2, Credits 6

Course type:  Lecture (Bachelor/Master)

Language: english

Exam: 1. 20.02.2014 (11:45 - 13:15 A704)

          2. 16.04.2014 (11:45 - 13:15 L602)


All materials are provided in ILIAS. Please register to the course.


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

This lecture will mainly follow the book "Guide to intelligent Data Analysis". There are roundabout 25 copies of it in the library (asbest free):  UB_UniKN or get it Online@Springer.

You can also order a cheaper copy(b/w) of the book if you are a member of the university of Konstanz. The service is called Springer's MyCopy. Via this link you see the button Buy a print copy(24,95€).


http://www.springer.com/series/3191 Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, Guide to Intelligent Data Analysis, How to Intelligently Make Sense of Real Data Series: Texts in Computer Science F. 1st Edition., 2010, XII, 397 p.

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
  • 30% of each exercise sheet
  • active participation in the exercise
  • Written exams at the end of the semester


Basic understanding of math and statistics