Course detail

Knowledge Discovery in Databases

FIT-ZZDAcad. year: 2010/2011

Not applicable.

Language of instruction

Czech

Number of ECTS credits

0

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. Elsevier Inc., 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

  • Bishop, CH. M.: Pattern Recognition and Machine Learning. Springer, 2006, 738 p. ISBN 978-0-387-31073-2.
  • Aggarwal, Ch.C. (ed.): Data Streams: Models and Algorithms. Advances in Database Systems. Springer, 2006, 358 p. ISBN 0387287590.
  • Příspěvky  v dostupných časopisech a sbornících konferencí (včetně dostupných v ACM Digital library, IEEE Digital library a jiných elektronických zdrojích).

Classification of course in study plans

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, elective

  • Programme VTI-DR-4 Doctoral

    branch DVI4 , any year of study, winter semester, elective

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

doc. Ing. Jaroslav Zendulka, CSc.

Syllabus

  1. Data preprocessing. 
  2. Data warehousing.
  3. Asociation analysis.
  4. Classification and prediction.
  5. Cluster analysis.
  6. Advanced data mining in 'classic' data sources.
  7. Mining in data streams.
  8. Data mining in time series and sequences.
  9. Mining in biological data.
  10. Data mining in graph structures.
  11. Multirelational data mining.
  12. Mining in object, spatial and multimedia data.
  13. Text mining and Web mining.

Project

13 hours, optionally

Teacher / Lecturer

doc. Ing. Jaroslav Zendulka, CSc.