Faculty of Information Technology, BUT

Course details

Knowledge Discovery in Databases

ZZN Acad. year 2008/2009 Winter semester 5 credits

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

Language of instruction

Czech

Completion

Credit+Examination (written)

Time span

39 hrs lectures, 13 hrs projects

Assessment points

Department

Lecturer

Instructor

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.

Study literature

  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3. 

Fundamental literature

  • Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
  • Dunham, M.H.: Data Mining. Introductory and Advanced Topics. Pearson Education, Inc., 2003, 315 p., ISBN 0-13088-892-3.

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.

Syllabus - others, projects and individual work of students

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

Progress assessment

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

Controlled instruction

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

Exam prerequisites

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

Course inclusion in study plans

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