Information Mining

Formalia


Targeted audience
  • DAI Hauptstudium with 8 credit points : Bereich "D"
  • ISE Master
  • Kommedia Master

Dates


Lectures

Date

Time

Place

Dienstag 12:30 - 15:00LC/137

Tutorials

Date

Time

Place

Tutor

Dienstag 15:05 - 16:00LC/137Dipl.-Inform. Thomas Beckers

Examination Dates


Exam

Date

Time

Place

-

Oral Exam

PeriodPlace
13.09.2010 - 16.09.2010LF/135
 

Description


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


  • The data mining part of the course is based on the book `Data Mining' by Ian Witten und Eibe Frank.
  • The text mining part is based on chapters 13-17 of the book Introduction to Information Retrieval by D. Manning, Prabhakar Raghavan and Hinrich Schütze.

Course structure:


  • Data Mining
    1. Introduction
    2. Input: Concepts, instances, attributes
    3. Output: Knowledge representation
    4. Algorithms: The basic methods
    5. Credibility: Evaluating what's been learned
    6. Implementations: Real machine learning schemes
    7. Transformations: Engineering the input and output
    8. Time series
  • Text Mining
    1. Text Representation
    2. Text clustering
    3. Text classification

Slides


Additional assignments / preparation for the oral exam