Course detail

Knowledge Discovery in Databases

FIT-ZZNAcad. year: 2010/2011

Not applicable.

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Jaroslav Zendulka, CSc.

Department

Department of Information Systems (UIFS)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Not applicable.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

Not applicable.

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

Not applicable.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic 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.  

 

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MPS , any year of study, winter semester, elective
    branch MBS , any year of study, winter semester, compulsory-optional
    branch MMI , any year of study, winter semester, elective
    branch MMM , any year of study, winter semester, elective
    branch MPV , 1. year of study, winter semester, compulsory-optional
    branch MBI , 2. year of study, winter semester, compulsory
    branch MGM , 2. year of study, winter semester, elective
    branch MSK , 2. year of study, winter semester, compulsory-optional
    branch MIS , 2. year of study, winter semester, compulsory-optional
    branch MIN , 2. year of study, winter semester, compulsory

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

doc. Ing. Jaroslav Zendulka, CSc.

Syllabus

  1. Introduction - motivation, fundamental concepts, data source and knowledge types.
  2. Data Warehouse and OLAP Technology for knowledge discovery.
  3. Data Preparation - methods.
  4. Data Preparation - characteristics of data.
  5. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
  6. Multi-level association rules, association mining 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 mining data stream, time-series and sequence data.
  11. Introduction to mining in graphs, spatial and multimedia data. 
  12. Mining in biological data.
  13. Text mining, mining the Web.

Project

13 hours, optionally

Teacher / Lecturer

doc. Ing. Jaroslav Zendulka, CSc.