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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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Introduction to DM Methodology CRISP-DM Data access and data manipulation Modelling Visualisation Cluster analysis, basket market analysis, association rules linear regression logistic regression Decision trees Neural Networks Factor analysis Web Mining Text Mining Use of DM and software tools
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Work with text (with textbook, with book), Methods of individual activities
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Learning outcomes
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The aim of the course is to acquaint students with possibilities of data mining, contents of individual phases of a data mining project as well as applied methods for a model creation.
Students will understand basic points of applied fields of data mining projects and will be able to operate individual project phases using the CRISP-DM methodology. In addition, they will know the base of the appropriate methods for a model creation and understand their right application in practice.
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Prerequisites
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unspecified
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Assessment methods and criteria
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Oral examination, Work-related product analysis, Creative work analysis
The assignment is granted upon elaboration of given tasks (minimum achievement of 60 percent is required) and submitting the seminar paper. Assessment methods: oral, written. The oral examination is based on defence of the seminar paper. The final assessment is comprised of the following proportions: work at seminars - 40 percent, defence of the seminar paper and responding to examiner questions - 60 percent; the written examination might also be considered.
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Recommended literature
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Berka, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.
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BERRY, M. - LINOFF G. Data Mining Techniques - For Marketing, Sales, and Customer Relationship Management. Indianapolis, John Wiley & Sons, 2004, 643 s.. 2004.
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GUIDICI P. Applied Data Mining - Statistical Methods for Business and Industry. Guildford, John Wiley & Sons, 2003, 364 s.. 2003.
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Petr, Pavel. Data Mining.. Pardubice: Univerzita Pardubice, 2006. ISBN 80-7194-886-1.
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PYLE, D. Data Preparation for Data Mining. San Diego, Academic Press, 1999, 540 s.. San Diego, 1999.
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