Lecture Intelligent Data Analysis

Summer Term 2017

News

A book on statistics (english) has been added to the literature section. Download is freely available from within the university.

Summary

General Information
Schedule and Rooms
Lecturers
Conditions for Certificates (Scheine) and Exams
Prerequisites
Slides
Assignment Sheets
Additional Material
Software
Data Sets
References
Links

General Information

This page contains information about the lecture "Intelligent Data Analysis" that is held in summer term 2017 by Prof. Dr. Rudolf Kruse. This page is updated during the course.

In many areas the overwhelmingly large volumes of data can hardly be assessed manually by a user: Think of market basket data collected in supermarkets. The analysis of this data however is important because they may reveal decisive information about e.g. the customers' buying behavior. Therefore, "intelligent" analysis techniques have to be devised that are capable to extract from illegible date comprehensible information.

In this lecture a multitude of methods are introduced that range from classical statistics (descriptive statistics, inferential statistics, parameter estimation, regression) over association rules, Bayesian classifiers, decision and regression trees, fuzzy data analysis to clustering techniques.

Schedule and Rooms

	Weekday	Time	Room	Begin
Lecture	Monday	11.15-12.45	G22A-111	03.04.2017
Exercise	Wednesday	13.15-14.45	G29-K058	05.04.2017
	Wednesday	15.15-16.45	G29-K058	05.04.2017

Lecturers

If you have questions regarding the lecture or exercise, please contact me (via e-mail if possible).

Prof. Dr. Rudolf Kruse, rudolf.kruse@ovgu.de
Christian Braune, christian.braune@ovgu.de

Conditions for Certificates (Scheine) and Exams

Certificate (Übungsschein): There are assignment sheets published every week. Assignments the solutions of which you want to present in the exercise lecture have to be ticked beforehand on a votation sheet that is handed out prior to every exercise lecture. If ticked, you may be asked to present your solution in front of class. The solutions need not necessarily be completely correct, however, it should become obvious that you treated the assignment thoroughly. You are granted the certificate (Schein), if (and only if) you

ticked at least two thirds of the assignments,
presented at least twice a solution during the exercise, and
passed a small colloquium (approx. 10 min) or a written test (if there are more than 20 students) after the course.
Additionally master students are required to take part in a data analytics challenge which will be presented during the exercises.

Exam: If you intend to finish the course with an exam, your are required to meet the certificate conditions. The exam will be written unless less than 10 students intend to do an exam. In such a case the written exam might be converted into an oral one.

Prerequisites

You should have background knowledge on fundamentals of computer science such as algorithms, data structures etc. Also, some insights into probability theory are highly recommended.

Slides

The lecture slides can be found below and will be constantly updated during the semester.

Introduction, Descriptive and Inductive Statistics
Classification
Clustering
Regression
Association Analysis (updated 26.06.2017)
Model Selection

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Assignment Sheets

The assignment sheets will be published weekly at this location.

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Additional Material

Feel free to check out the following supplementary material that augment the lecture and exercise.

Introduction to Statistics (German)
Paper on Bayesian classifiers and fuzzy clustering
Paper on Decision Trees (German)
Pseudo code of the Apriori Algorithm
Derivation of polynomial regression (German)
Chi^2 Test (German)
Answer Trees (German)

Software

Here you find links to programs with which simple data analysis tasks can be solved.

Information Mining tools

Data Sets

Here you find some example data sets that can be fed into the algorithms above.

References

Foundations of Intelligent Data Analysis: Making Practical Sense of Real Data
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
Springer-Verlag, London, 2010
Intelligent Data Analysis: An Introduction (2. edition)
D. Hand and M. Berthold (eds.)
Springer-Verlag, Berlin, 2002
Introduction to Statistics
W. Härdle, S. Klinke, B. Rönz
ISBN: 978-3-319-17703-8 (Print) 978-3-319-17704-5 (Online)
Springer Switzerland, 2015
Elementare Einführung in die angewandte Statistik
K. Bosch
Vieweg, Wiesbaden, 2000
Angewandte Statistik (9. Auflage)
L. Sachs
Springer, Berlin, 1999
Machine Learning
T. Mitchell
McGraw Hill, New York, NY, USA 1997
Data Mining Techniques. For Marketing, Sales, and Customer Support.
M.J. Berry and G.S. Linoff
J. Wiley & Sons, Chichester, United Kingdom 1997
Data Mining - Theoretische Aspekte und Anwendungen
G. Nakhaeizadeh (Hrsg.)
Physica-Verlag, Heidelberg, 1998
Data Mining Methods for Knowledge Discovery
K. Cios, W. Pedrycz, R. Swiniarski
Kluwer, Dordrecht, Netherlands 1998