Course detail
Knowledge Discovery in Databases
FIT-ZZDAcad. year: 2010/2011
Not applicable.
Language of instruction
Czech, English
Mode of study
Not applicable.
Guarantor
Department
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
Type of course unit
Lecture
39 hod., optionally
Teacher / Lecturer
Syllabus
- Data preprocessing.
- Data warehousing.
- Asociation analysis.
- Classification and prediction.
- Cluster analysis.
- Advanced data mining in 'classic' data sources.
- Mining in data streams.
- Data mining in time series and sequences.
- Mining in biological data.
- Data mining in graph structures.
- Multirelational data mining.
- Mining in object, spatial and multimedia data.
- Text mining and Web mining.