Course details

Knowledge Discovery in Databases

ZZD Acad. year 2005/2006 Winter semester

Current academic year

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

Zendulka Jaroslav, doc. Ing., CSc.

Language of instruction

Czech, English

Completion

Examination

Time span

  • 39 hrs lectures
  • 13 hrs projects

Department

Department of Information Systems (UIFS)

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.

Prerequisite knowledge and skills

Students should have some knowledge of concepts and terminology related to database systems and they shoul also have some programming experience. Some preliminary background in statistics and will be helpful, but not necessary.

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.

Syllabus of lectures

  • Introduction - motivation, fundamental concepts, data source and knowledge types.
  • Data Warehouse and OLAP Technology for Data Mining.
  • Data Preparation.
  • Data Mining Systems - task specification, data mining query languages, system architectures.
  • Concept Description: Characterization and Comparison.
  • Mining Association Rules in Transaction Data.
  • Mining Association Rules in Relational Databases and Warehouses.
  • Classification - decision tree, Bayesian classification, using neural networks for classification.
  • Other Classification Methods. Prediction.
  • Cluster Analysis.
  • Mining Complex Types of Data - data mining inobject, spatial, and text data.
  • Mining in Multimedia Data, Time Sequences, and Mining the WWW.
  • Applications and Trends in Data Mining.

Progress assessment

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Controlled instruction

Lectures and the project.

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