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 - 15:00 | LC/137 |
Tutorials
Date | Time | Place | Tutor |
| Dienstag | 15:05 - 16:00 | LC/137 | Dipl.-Inform. Thomas Beckers |
Examination Dates
Exam
Date | Time | Place |
| - |
Oral Exam
| Period | Place |
|---|---|
| 13.09.2010 - 16.09.2010 | LF/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
- Introduction
- Input: Concepts, instances, attributes
- Output: Knowledge representation
- Algorithms: The basic methods
- Credibility: Evaluating what's been learned
- Implementations: Real machine learning schemes
- Transformations: Engineering the input and output
- Time series
-
Text Mining
- Text Representation
- Text clustering
- Text classification
Exercises
Useful Links for RapidMiner