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Lecturer(s)
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Petr Pavel, doc. Ing. Ph.D.
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Course content
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unspecified
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Dialogic (discussion, interview, brainstorming), Work with text (with textbook, with book)
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Learning outcomes
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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.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Home assignment evaluation, Student performance assessment
Students have to take part in seminars.
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Recommended literature
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Aggarwal, Charu C. Mining text data. New York: Springer Science+Business Media, 2012. ISBN 978-1-4614-3222-7.
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Feldman. The text mining handbook : advanced approaches in analyzing unstructured data. New York, 2007.
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MINER, G. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Amsterdam, 2012.
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Tufféry, Stéphane. Data mining and statistics for decision making. Chichester: John Wiley & Sons, 2011. ISBN 978-0-470-68829-8.
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WITTEN, I.H., FRANK, E., HALL, M.A. Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, 2011.
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