Course: Data Mining II

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Course title Data Mining II
Course code USII/CDM2
Organizational form of instruction Lecture
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 5
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
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

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Work with text (with textbook, with book), Methods of individual activities
  • Home preparation for classes - 56 hours per semester
  • Preparation for an exam - 20 hours per semester
  • Contact teaching - 14 hours per semester
  • Preparation for a credit (assessment) - 20 hours per semester
  • Term paper - 40 hours per semester
Learning outcomes


Prerequisites
unspecified

Assessment methods and criteria
Oral examination, Written examination, Work-related product analysis, Creative work analysis, Self project defence

The assignment is granted upon elaboration of given tasks at seminars (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 - 60 percent; the written examination might also be considered.
Recommended literature
  • Berka, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.
  • BERRY, M. - LINOFF G. Data Mining Techniques - For Marketing, Sales, and Customer Relationship Management. Indianapolis, John Wiley & Sons, 2004, 643 s.. 2004.
  • GUIDICI P. Applied Data Mining - Statistical Methods for Business and Industry. Guildford, John Wiley & Sons, 2003, 364 s.. 2003.
  • Petr, Pavel. Data Mining.. Pardubice: Univerzita Pardubice, 2006. ISBN 80-7194-886-1.
  • Petr, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2014. ISBN 978-80-7395-872-5.
  • Petr, Pavel. Metody Data Miningu.. Pardubice: Univerzita Pardubice, 2015. ISBN 978-80-7395-873-2.
  • PYLE, D. Data Preparation for Data Mining. San Diego, Academic Press, 1999, 540 s.. San Diego, 1999.
  • 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) Category of Branch/Specialization Recommended year of study Recommended semester