Knowledge Discovery from Databases

Lecturer:

Tutor:

Supervisor:

Department of Software Engineering

Synopsis:

Students will learn to solve the problems of data mining in a complex way, starting from preprocessing of raw data to presentation of mined knowledge and deployment of data mining solutions in a company. They will get skills to apply their knowledge of data mining to solve various problems. Furthermore, they will acquaint with the GUHA method and observational calculi.

Requirements:

Data preprocessing for data mining, pattern recognition, computational intelligence methods.

Syllabus of lectures:

1. KDD (Knowledge Discovery from Databases), CRISP-DM.

2. Self organizing data mining.

3. GUHA Method.

4. The LISp-Miner system.

5. Observational calculi.

6. Applications of selected methods of knowledge engineering in KDD.

7. Presentations of companies and institutions describing their use and handling of knowledge.

Syllabus of tutorials:

1. Individual projects.

Study Objective:

The aim of this module is to bring together and to systematize the knowledge and skills received in specialization-specific knowledge engineering modules, as well as to demonstrate its use on real-world problems.

Study materials:

1. Lemke, F., Müller, J. A. ''Self Organising Data Mining''. Books on Demand, 2000. ISBN 3898118614.

2. Hajek, P., Havranek, T. ''Mechanizing hypothesis formation: Mathematical foundations for a general theory''. Springer-Verlag, 1978. ISBN 0387087389.

3. J. Rauch: Logic of Association Rules. Applied Intelligence 22 (2005), pp. 9 28

4. J. Rauch: Classes of Association Rules - an Overview. In T. Lin et al. (Eds.): Datamining: Foundations and Practice. Springer 2008, Studies in Computational Intelligence, Volume 118. pp. 283297.

5. Sborníky konferencí z oblasti KDD, zejména ECML/PKDD, ICDM, PAKDD.

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans: