Course detail
Knowledge Discovery in Databases
ÚSI-RTZZDAcad. year: 2019/2020
The course aims to the basic concepts concerning knowledge discovery in data, relation of knowledge discovery and data mining, data sources for knowledge discovery, principles and techniques of data pre-processing for mining, systems for knowledge discovery in data, data mining query languages. It also focuses on data mining techniques – characterization and discrimination, association rules, classification and prediction, clustering, complex data type mining, trends in data mining. Developing a data mining project by means of an available data mining tool.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Department
Learning outcomes of the course unit
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.
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
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.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
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.
Recommended reading
Classification of course in study plans