Knowledge Discovery in Databases

Language of Instruction:Czech
Completion:examination (verbal)
Type of
Guarantor:Zendulka Jaroslav, doc. Ing., CSc. (DIFS)
Lecturer:Zendulka Jaroslav, doc. Ing., CSc. (DIFS)
Faculty:Faculty of Information Technology BUT
Advanced Database Systems (PDB), DIFS
Knowledge Discovery in Databases (ZZN), FIT
Substitute for:
Knowledge Discovery in Databases (ZZD), FIT
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.
  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.
Learning outcomes and competencies:
  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.
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.
Syllabus - others, projects and individual work of students:
  • Reading up and treatment of a selected scientific paper concerning knowledge discovery in a field related to the student's PhD thesis.
Fundamental literature:
  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2001, 550 p.
  • Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall, 2002, 336 p
Study literature:
  • Fayyad U.M. (Ed.): Advances in Knowledge Discovery and Data Mining. AAAI Press/the MIT Press, 1996, 560 p.
  • Weiss, S.M., Indurkhya, N.: Predictive Data Mining. Morgan Kaufman Publishers, Inc., 1998, 238 p.
  • Weiss, S.M., Indurkhya, N.: Predictive Data Mining. Morgan Kaufman Publishers, Inc., 1998, 238 p.
  • Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, 2001, 425 pp.
  • Chakrabarti, S.: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann Publishers, 2002, 352 p.
  • Theodoridis, S.K. : Pattern Recognition. Academic Press. 1998, 624.
Controlled instruction:
  Lectures and the project.
Progress assessment:
  Control questions during lectures.

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