Course: Advanced Methods of Data Mining

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Course title Advanced Methods of Data Mining
Course code FES/DPMDI
Organizational form of instruction Lecture
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 Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Petr Pavel, doc. Ing. Ph.D.
Course content
unspecified

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book)
Learning outcomes
The course is aimed at clarification of theoretical principals and solution of practical problems with application of Data Mining, Web Mining and Text Mining. Attention is paid to tasks of data transformation, pre-processing and verification, furthermore selection of the appropriate methods, process evaluation and results' interpretation.
Student will be able to suitably apply methods of spatial analyses and visualisation methods during solving spatially-oriented tasks.
Prerequisites
unspecified

Assessment methods and criteria
Home assignment evaluation, Student performance assessment

Students have to take part in seminars.
Recommended literature
  • Aggarwal, Charu C. Mining text data. New York: Springer Science+Business Media, 2012. ISBN 978-1-4614-3222-7.
  • Feldman. The text mining handbook : advanced approaches in analyzing unstructured data. New York, 2007.
  • MINER, G. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Amsterdam, 2012.
  • Tufféry, Stéphane. Data mining and statistics for decision making. Chichester: John Wiley & Sons, 2011. ISBN 978-0-470-68829-8.
  • WITTEN, I.H., FRANK, E., HALL, M.A. Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, 2011.


Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester
Faculty of Economics and Administration - (2014) Economy - -
Faculty of Economics and Administration Informatics in Public Administration (2014) Economy - -