Course details
Knowledge Discovery in Databases
ZZD Acad. year 2003/2004 Winter semester
Basic concepts concerning knowledge discovery in data, relation of knowledge discovery and data mining. Data sources for knowledge discovery. Principles and techniques of data preprocessing for mining. Systems for knowledge discovery in data, data mining query languages. Data mining techniques - characterization and discrimination, association rules, classification and prediction, clustering. Complex data type mining. Trends in data mining. Treatment of a given topic and its presentation.
Guarantor
Language of instruction
Completion
Time span
- 39 hrs lectures
Department
Subject specific learning outcomes and competences
Students get a broad, yet in-depth overview of the field of data mining and knowledge discovery. They get a deeper view mainly in the field related to the topic of their thesis.
Learning objectives
To familiarize students with knowledge discovery in data sources, to explain useful knowledge types and the steps of the knowledge discovery process, and to familiarize them with techniques and tools used in the process.
Study literature
- 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).
Fundamental 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.
Progress assessment
Control questions during lectures.