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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Jaroslav Zendulka, CSc.

Department

Faculty of Information Technology (FIT)

Learning outcomes of the course unit

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.

Prerequisites

Nejsou požadovány

Co-requisites

Not applicable.

Planned learning activities and teaching methods

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

Assesment methods and criteria linked to learning outcomes

Půlsemestrální písemná zkouška, formulace dolovací úlohy, obhajoba projektu. Udělení zápočtu je podmíněno vypracováním projektu a ziskem minimálně 24 bodů za bodované aktivity v průběhu semestru.

Course curriculum

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.

Work placements

Not applicable.

Aims

Seznámit studenty s problematikou získávání znalostí z různých typů datových zdrojů, vysvětlit typy užitečných znalostí a jednotlivé kroky procesu získávání znalostí z dat a seznámit je s technikami, algoritmy a nástroji používanými při tomto procesu.

Specification of controlled education, way of implementation and compensation for absences

Není specifikováno.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
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.

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme RRTES_P Master's

    specialization RRTS , 2. year of study, winter semester, compulsory-optional

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

doc. Ing. Jaroslav Zendulka, CSc.

Exercise

13 hours, optionally

Teacher / Lecturer

doc. Ing. Jaroslav Zendulka, CSc.