Course: Data Mining

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Course title Data Mining
Course code KIN/XDM
Organizational form of instruction no contact
Level of course Doctoral
Year of study not specified
Semester Winter and summer
Number of ECTS credits 10
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction unspecified
Work placements unspecified
Recommended optional programme components None
Lecturer(s)
  • Beránek Ladislav, doc. Ing. CSc.
Course content
1. Introduction into data mining 2. Data sources. Relational DB. OLAP. Data warehouses. 3. Fundamentals of statistics and 4. Statistics. Pivot tables. Regression analyzes. Clustering. 5. Data preparing 6. Machine learning. 7. Decision trees. 8. Association rules. 9. Decision rules. 10. Neural networks, Bayesian classification. 11. Result evaluation. 12. Data mining software systems overview.

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book), Laboratory
Learning outcomes
The course is intended as an introduction into data mining. The students will be given an overview of OLAP and data warehousing, data mining (association rules mining, classification, clustering?). Basic principles and steps of data mining process will be explained through simple examples; solutions of practical tasks will be demonstrated by means of available data mining open-source tools such as Rapid Miner, Orange, WEKA.
Student will be familiarized with the steps of the knowledge discovery process, and with techniques, algorithms and tools used in the process od knowledge discovery in databases. Student will be able to solve simple practical data mining tasks
Prerequisites
none

Assessment methods and criteria
Analysis of student's work activities (technical works), Combined exam

Working-out a data mining project by means of an available data mining tool.
Recommended literature
  • Berka, P. Dobývání znalostí z databází. Praha: Academia, 2003. 366 s. ISBN 80-200-1062-9.
  • Berka Petr. Dobývání znalostí z databází. Praha, 2003. ISBN 80-200-1062-9.
  • Humphries, M., Hawkins, W.,M., Dy. M.C. Data warehousing Návrh a implementace. Computer Press, 2002. ISBN 8072265601.. Computer Press, 2002. ISBN 8072265601.
  • LACKO, M. Databáze: datové sklady, OLAP a dolování dat. Computer Press, 2003. ISBN 80-7226-969-0.. Computer Press, 2003. ISBN 80-7226-969-0.
  • MARKOV, Zdravko, LAROSE, T. Daniel. Data MIning the Web : Uncovering Patterns in Web Content, Structure, and Usage. New Jersey : JOhn Wiley & Sons, 2007. ISBN 978-0-471-66655-4.
  • WEKA. Data Mining Software in Java [online]..
  • Weka 3. Data Mining Software in Java [online].. 1998.
  • WITTEN, H. Ian, EIBE, Frank. Data Mining : Practical Machine Learning Tools and Techniques. San Francisco : Morgan Kaufman, 2005. ISBN 978-0-12--088407-.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Education Study plan (Version): Information and communication technology in education (2) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Education Study plan (Version): Information and communication technology in education (2) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Education Study plan (Version): Information and communication technology in education (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: -
Faculty: Faculty of Education Study plan (Version): Information and communication technology in education (1) Category: Pedagogy, teacher training and social care - Recommended year of study:-, Recommended semester: -