Course details

Knowledge Discovery in Databases

ZZN Acad. year 2006/2007 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

  1. Introduction - motivation, fundamental concepts, data source and knowledge types.
  2. Data Warehouse and OLAP Technology for knowledge discovery.
  3. Data Preparation.
  4. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
  5. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
  6. Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
  7. Classification by means of neural networks, SVM classifier, other classification methods, prediction.
  8. Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods.
  9. Other clustering methods.
  10. Mining stream, time-series and sequence data.
  11. Graph mining, social network analysis, multirelational data mining.
  12. Mining object , spatial and multimedia data, text mining, mining the Web.
  13. 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.

Back to top