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Lecturer(s)
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Kašparová Miloslava, Ing. Ph.D.
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Petr Pavel, doc. Ing. Ph.D.
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Course content
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Introduction to the DM. Methodologies DM. Phase and operation methodology CRISP. Understanding data. Data preparation to simulation. Principles creation of models. Proposal model.
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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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Goal is introduce students with possibilities data miningu (DM). Present problems definition goal and technology for DM, question selection sources data and their preparation for simulation. Attention paid to problems selection and transformation variable, processing, evaluation and check DM models.
Students will be able to define individual phases of a data mining project and its content. Using software tools they will know how to solve basic tasks in the area of data preparation and choose the appropriate methods for a model creation.
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Prerequisites
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Bases work with database, knowledge from mathematics to the extent of subject 1. and 2. class.
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Assessment methods and criteria
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Oral examination, Work-related product analysis, Creative work analysis
Requirements to inclusion: work up engaged exercise on exercising and seminar with fruitfulness mine. 60%, transfer semestral work according to setting. Requirements to examination (inclusive forms examination): verbal State one's case (rout) of the semestral work. Resulting evaluation is given to ratio 40% exercising and seminars, 50% case for the defence final work and reaction to questions examiner, 10% rout semestral work.
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Recommended literature
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Berry, Michael J. A. Data mining techniques : for marketing, sales, and customer relationship management. Indianapolis: Wiley, 2004. ISBN 0-471-47064-3.
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Berry, Michael J. A. Mastering data mining. New York: John Wiley & Sons, 2000. ISBN 0-471-33123-6.
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Giudici, Paolo. Applied data mining : statistic methods for business and industry. Chichester: Wiley, 2003. ISBN 0-470-84678-X.
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Pyle, Dorian. Data preparation for data mining. San Francisco: Morgan Kaufmann, 1999. ISBN 1-55860-529-0.
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