Information Mining

Teaching personnel



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 Kap. 1-7







Tuesday 12:30 - 14:55 18.10. LB/131







Tuesday 15:00 - 15:45 25.10. LB/131Dr. Ahmet Aker

Examination Dates

Oral Exam

27.03.2017 - 31.03.2017LE/313

As usual, you have to register at the Prüfungsamt for the exams. Normally, you have to do nothing else!

We will schedule your exam during the period specified above. The personal appointments for the oral exams will be announced at our Web site on the last Tuesday before the exam week

Only if (and only then!!!) 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, and no later than 2 weeks before the exam period.
  • Most likely, exams will only take place from Monday-Thursday, so requests for Friday cannot be considered.
  • You should be available full-day on at least one of these 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 July 1.

Emails not following the rules from above will not be answered (like those saying 'Please give me an appointment for my exam in ...', or emails not originating from an mail account)



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 purpose, machine learning methods are applied.

Lecture material

Slides as well as sheets for the exercises can be obtained through ILIAS. For this please follow the following steps:

  • Shibboleth Login -> Login with your university login information
  • Scroll: Magazin -> Information Systems -> Information Mining
  • Click the button "Beitreten"

Lecture material

Course structure

Lehrangebot des Fachgebiets

Introduction to IM:

  • Data Mining
    • Chapter 1: Introduction
    • Chapter 2: Imput: Concepts, Instances, Attributes
    • Chapter 3: Output: Knowledge Representation
    • Chapter 4: Algoriths: The Basic Methods
    • Chapter 5: Credibility: Evaluating what's been learned
    • Chapter 6: Implementation: Real machine learning schemes
    • Chapter 7: Data transformations
    • Chapter 8: Ensemble learning
  • Deep learning
  • Big Data
  • Mining Sequential Patterns:
  • Graph Mining
  • Process Mining


The sheets for the exercises can be obtained through ILIAS.