Information Mining


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







Dienstag 10:15 - 12:40 14.10.2014 LC/137







Dienstag 12:45 - 13:30 21.10.2014 LC/137Dr.-Ing. Dipl.-Inform. Vu Tran

Examination Dates

Oral Exam

14.09.2015 - 18.09.2015LF/135

The personal appointments for the oral exams will be announced at our Web site after the end of the withdrawal period (1 week).
In case 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 August 30.
  • 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 July 1.


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


Einige Folienkapitel sind aus Copyright-Gründen nur innerhalb der UDE erreichbar!

Lehrangebot des Fachgebiets

Einführung: odp pdf

  • Data Mining
    • Kapitel 1: Einführung odp pdf
    • Kapitel 2: Eingabe: Konzepte, Instanzen, Attribute odp pdf
    • Kapitel 3: Ausgabe: Wissensrepräsentation odp pdf
    • Kapitel 4: Algorithmen: Die grundlegenden Methoden odp pdf
      • Bayes'sche Netzwerke: odp pdf
    • Kapitel 5: Glaubwürdigkeit: Auswertung des Gelernten odp pdf
    • Kapitel 6: Implementierung: Maschinelles Lernen in der Praxis odp pdf
    • Kapitel 7: Transformationen: Aufbereitung der Ein- und Ausgabe odp pdf
    • Kapitel 8: Ensemble-Lernen odp pdf
    • Random Forests (nicht prüfungsrelevant): pdf
    • Data Warehouse: odp pdf
    • Big Data (prüfungsrelevant bis S. 68): (Lokal) (Slideshare)
  • Zeitreihen
  • Process Mining
  • Graph Mining
    • Graph Mining Techniques (nur bis S. 100 prüfungsrelevant): 1:1 4:1
  • Deep Learning