Data Mining (HWS 2012)

The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:

  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Sequential Patterns
  • Text Mining
  • Systems and Applications (e.g. Retail, Finance, Web Analysis)

The course consists of a lecture together with accompanying practical exercises as well as student team projects.  In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets. The team projects take place in the last third of the term. Within the projects, students realize more sophisticated data mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.

Exam Review: Monday 11th March 2013, room B6, 26 A 2.06, from 4.00 pm to 4.30 pm.

Time and Location

  • Lecture: Wednesday, 15:30 - 17:00, room C014 in A5, start: 5.9.2012
  • Exercise: Thursday, 10.15 - 11.45, room C015 in A5,  start: 6.9.2012

Instructor

  • Prof. Dr. Christian Bizer

Final exam

  • 50 % written exam
  • 50 % project work

Slides and Excercises

  • The lecture slides and excercises are provided in ILIAS.

Participation

  • The course is open to students of the Master Business Informatics and Lehramt Informatik.
  • There is no restriction on the number of participants.

Outline

Week Topic Lecture Topic Exercise
5.9.2012 Introduction to Data Mining Introduction to RapidMiner
12.9.2012 Cluster Analysis Exercise Clustering
19.9.2012 Cluster Analysis Exercise Clustering
26.9.2012 Classification  Exercise Classification
3.10.2012 Classification  Exercise Classification
10.10.2012 Association Analysis Exercise Association Analysis
17.10.2012 Sequential Patterns Text Mining
24.10.2012 Exercise Text Mining Introduction to student projects
31.10.2012 Presentation of project outlines Project work
7.11.2012 Project work Project work
14.11.2012 Project work Project work
21.11.2012 Project work Project work
28.11.2012 Presentation of project results Presentation of project results
5.12.2012 Presentation of project results Preparation for the exam 

 Literature 

  1. Pang-Ning Tan, Michael Steinback, Vipin Kumar: Introduction to Data Mining, Pearson.
  2. Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.
  3. Bing Liu: Web Data Mining, 2nd Edition, Springer.

Software