Czech

Not applicable.

Students will gain a wide yet sufficiently deep overview of the field of knowledge acquisition from databases.
They will be able to both use and develop tools for knowledge acquisition.
Students will learn specialised terminology in both Czech and English.
Students will gain experience with working on projects in a small team.
Students will improve their skills in the area of the presentation and defence of project results.

Not required.

Not applicable.

Tuition takes place via lectures and seminars. The lectures focus on the explanation of basic principles, the methods of the given discipline, problems and example solutions.

A mid-semester written exam, formulation of a data-mining task, defence of a project. The credit is awarded on the basis of the completion of a project, and the attainment of at least 24 points for graded activities during the semester.

1. Introduction – motivation, fundamental concepts, types of data source and acquired knowledge, 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.

To acquaint students with issues concerned with the gaining of knowledge from various types of data sources, to explain the types of useful knowledge and the individual steps of knowledge acquisition from data and acquaint students with the techniques, algorithms and tools used in this process.

Not specified.

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.