Course details

Knowledge Discovery in Databases

ZZN Acad. year 2007/2008 Winter semester 5 credits

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. Working-out a data mining project by means of an available data mining tool.

Guarantor

Zendulka Jaroslav, doc. Ing., CSc.

Language of instruction

Czech

Completion

Credit+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 are able both to use and to develop knowledge discovery tools.

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, algorithms and tools used in the process.

Prerequisite knowledge and skills

There are no prerequisites

Fundamental literature

  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 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

Duty credit consists of working-out the project and of obtaining at least 25 points for activities during semester.

Controlled instruction

A mid-term test, formulation of a data mining task, presentation of the project.

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