Datamining

Garant předmětu:

Lecturer:

Tutor:

Supervisor:

Department of Applied Informatics in Transportation

Synopsis:

Types of data sources and acquired knowledge, data stores and OLAP technology for knowledge acquiring from data, data preprocessing at knowledge acquiring process, datamining systems, classes characteristics mining, mining of asocial rules from data stores and databases, classification (decision-making tree, Bayes classification, use of neuron networks). Prediction. Cluster analysis. Mining in complex structured data, multimedial dbf, www.

Requirements:

database structure, normalisation, views, SQL, data types, knowledge of IS building

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Acquisition of knowledge in the following fields: knowledge acquisition from different data sources, procedure of knowledge acquisition from data, tools used at this process.

Study materials:

Daniel T. Larose: Discovering Knowledge in Data: An Introduction to Data Mining

Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers

Dunham, M. H.: Data Mining: Introductory and Advanced Topics. Prentice Hall, 2002

Fayyad U. M. (Ed.): Advances in Knowledge Discovery and Data Mining. AAAI Press / the MIT Press, 1996

Note:

Further information:

No time-table has been prepared for this course

The course is a part of the following study plans: