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Principles of Data Mining

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First lecture on monday 21 october.

 

This course is a shorter replacement of the lecture "Data Mining: Foundations". Please note that it is not possible to credit both courses in your studies. You may attend the course but will not receive an examination allowance for it.

Overview

Leistung: SWS : 2, Credits 4

Course type:  Lecture (Bachelor/Master)

Language: english

Exam: 

Content

The course aims at introducing main problems of modern data mining and methods to solve them. It focuses on basic approaches to clustering, classification, association mining and data explanation. Many application examples help to understand how the algorithms work.

21.10

Intro and Overview

28.10

Data and Project Understanding

04.11

Modeling and Data Preparation

11.11

Exercise 1

18.11

Clustering/Hierarchical Clustering

25.11

Association Rules

02.12

Exercise 2

09.12

Naive Bayes Classifier/Regression

16.12

Exercise 3

13.01

Decision & Regression Trees

20.01

Rule Learning/Recapitulation

27.01

Exercise 4

03.02

Q&A

10.02

Exam

 

Literature

http://www.springer.com/series/3191 Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, Guide to Intelligent Data Analysis, How to Intelligently Make Sense of Real Data Series: Texts in Computer Science F. 1st Edition., 2010, XII, 397 p.

Han J., Kamber M., „Data Mining: Concepts and Techniques“, Morgan Kaufmann Publishers, August 2000.

Ester M., Sander J., „Knowledge Discovery in Databases. Techniken und Anwendungen“, Springer, 2000.

Hand D.J., Mannila H., Smyth P., „Principles of Data Mining“, MIT Press, 2001.

Mitchell T. M., „Machine Learning“, McGraw-Hill, 1997.

Witten I. H., Frank E., „Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations“, Morgan Kaufmann Publishers, 2000.

Course criteria

  • 50% votations exercise sheets
  • active participation in the exercise
  • Oral exam or written exams at the end of the semester

Prerequisites

Basic mathematical skills (statistics)