Information Mining


Targeted audience
  • Angewandte Informatik Master with 6 credit points
  • Komedia Master with 6 credit points
  • ISE Master with 6 credit points
  • BWL Master with 2+1 hours per week and 4 credit points : nur Data-Mining-Teil







Dienstag 12:30 - 14:55 20.10.2015 LB/131







Dienstag 15:00 - 15:45 27.10.2015 LB/131Michael Rist, M.Sc.

Examination Dates

Oral Exam

7.03.2016 - 10.03.2016LE/313

As usual, you have to register at the Prüfungsamt for the exams, and then we will schedule your exam. The personal appointments for the oral exams will be announced at our Web site after the end of the withdrawal period on March 1.
If (and only if!!!) you are not available on single days of the examination period, please send an email to our secretary Fr. Ufermann. Please observe the following guidelines:

  • Do not mail us earlier than 4 weeks before the exam date, and no later than February 22.
  • Most likely, exams will only take place from Tuesday-Thursday, so requests for Monday or Friday cannot be considered.
  • You should be available full-day on at least one of these three days - in case you are available for a half day only, we will try our best.
  • In case you registered for 2 exams, both will be held together.
  • In case you are not at all available in the above period, we will try to find a separate exam date for you. Only in this case, send an email directly to Prof. Fuhr, but not before January 15.

Example exam questions



Information Mining deals with the extraction on implicit information from raw data (Data Mining) or text (Text Mining). The goal is the development of methods for analyzing databases and discovering useful information by means of abstraction. For this pupose, machine learning methods are applied.

Lecture material


For copyright reasons, some chapters are accessible from the university network only!

Lehrangebot des Fachgebiets

Introduction to IM: pdf

  • Data Mining
    • Chapter 1: Introduction odp pdf
    • Chapter 2: Imput: Concepts, Instances, Attributes odp pdf
    • Chapter 3: Output: Knowledge Representation odp pdf
    • Chapter 4: Algoriths: The Basic Methods odp pdf
    • Chapter 5: Credibility: Evaluating what's been learned odp pdf
    • Chapter 6: Implementation: Real machine learning schemes odp pdf
    • Chapter 7: Data transformations odp pdf
    • Chapter 8: Ensemble learning odp pdf
    • Random Forests (not relevant for exam) pdf
    • Data Warehouse: (not relevant for exam) odp pdf
    • Big Data: (Lokal) (Slideshare)
    • Wiederholung
  • Mining Time Series and Sequences
    • Introduction odp pdf
    • Mining of Time Series Data: odp pdf
    • Mining Sequential Patterns: ppt pdf
  • Process Mining
  • Graph Mining
    • Graph Mining Techniques (only pages 1-101 relevant for exam): 1:1 4:1
  • Deep learning (not relevant for exam) pdf

More exercises