Knowledge Discovery in Databases

Guarantor

Zendulka Jaroslav, doc. Ing., CSc.

Language of instruction

Czech

Completion

Credit+Examination

Time span

Department

Department of Information Systems (UIFS)

Fundamental literature

Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.

Syllabus of lectures

Introduction - motivation, fundamental concepts, data source and knowledge types.
Data Warehouse and OLAP Technology for knowledge discovery.
Data Preparation - methods.
Data Preparation - characteristics of data.
Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
Classification by means of neural networks, SVM classifier, other classification methods, prediction.
Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods. Other clustering methods.
Introduction to mining data stream, time-series and sequence data.
Introduction to mining in graphs, spatial and multimedia data.
Mining in biological data.
Text mining, mining the Web.

Course inclusion in study plans