Czech

Not applicable.

Students will gain knowledge in the field of databases.
They will be able to use and develop knowledge tools.
Students will learn terminology in Czech and English.
Students will gain experience while implementing projects in a small team.
Students will improve their presentation and defence of the project results.

There are no prerequisites required.

Not applicable.

Teaching is carried out through lectures that consist of interpretations of basic principles, methodology of given discipline, problems and their exemplary solutions.

A mid-term written test, formulation of a data mining task, defence of the project. Duty credit consists of working-out the project and of obtaining at least 24 points for evaluated activities during the semester. The minimal number of points, which must be obtained from the final exam, is 20.

1. Introduction – motivation, fundamental concepts, data source and knowledge types, methodology.
2. Data Warehouse and OLAP Technology for knowledge discovery.
3. Data Preparation – methods.
4. Data Preparation – data characteristics.
5. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent item set rummaging methods.
6. Multi-level association rules, association rummaging and correlation analysis, constraint-based association rules.
7. Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
8. Classification by means of neural networks, SVM classifier, other classification methods, prediction.
9. Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods. Other clustering methods.
10. Introduction to rummaging data stream, time-series and sequence data.
11. Introduction to rummaging in graphs, time-spatial and multimedia data.
12. Mining in biological data.
13. Text rummaging, rummaging the Web.

Not applicable.

The goal of the course is to familiarize students with knowledge development in data sources, to explain useful knowledge types and the steps of the knowledge development process, and to familiarize them with techniques, algorithms and tools used in the process.

Not applicable.

Not applicable.

Not applicable.

Berka, P.: Dobývání znalostí za databází. Academia, 2003, 366 s., ISBN 80-200-1062-9.
Dunham, M.H.: Data Mining. Introductory and Advanced Topics. Pearson Education, Inc., 2003, 315 p.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.

Not applicable.