| Course Unit Code | 157-0386/01 |
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| Number of ECTS Credits Allocated | 5 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Summer Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| HUD0118 | doc. Dr. Ing. Miroslav Hudec |
| Summary |
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| The aim of the course is to understand and master the main approaches and methods for mining and interpreting information and knowledge from the data. The lectures provide a theoretical basis for understanding data mining. The seminars provide space for demonstrating tasks, examining various cases and discussion. |
| Learning Outcomes of the Course Unit |
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| The aim of the course is to understand and master the main approaches and methods for mining and interpreting information and knowledge from the data. The lectures provide a theoretical basis for understanding data mining. The seminars provide space for demonstrating tasks, examining various cases and discussion. |
| Course Contents |
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1. Introduction into data mining (definition of data mining, relation to the other scientific disciplines, clarification of the basic concepts).
2. Data types (numeric, categorical, text, fuzzy data). Logical, statistical and algebraic view of data. Categorization of data mining requirements.
3. Steps of data mining: data pre-processing, data cleaning, mining and interpretation of results.
4. Methods and properties of direct and indirect data mining. Task categorization of tasks and classification of methods.
5. Classical and flexible classification, classical and flexible aggregation.
6. Association rules, decision trees and network analysis.
7. Statistical and logical data summaries.
8. Computational intelligence in data mining.
9. Aggregation and evaluation of opinions.
10. Basic procedures of text mining, text categorization, classification of text documents. |
| Recommended or Required Reading |
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| Required Reading: |
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BERKA, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.
HUDEC, Miroslav. Fuzzy logika pre hospodársku informatiku. Bratislava: Ekonóm, 2015. ISBN 978-80-225-4100-8.
SKALSKÁ, Hana. Data mining a klasifikační modely. Hradec Králové: Gaudeamus, 2010. ISBN: 978-80-7435-088-7.
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BERKA, Petr. Dobývání znalostí z databází. Praha: Academia, 2003. ISBN 80-200-1062-9.
HUDEC, Miroslav. Fuzzy logika pre hospodársku informatiku. Bratislava: Ekonóm, 2015. ISBN 978-80-225-4100-8.
SKALSKÁ, Hana. Data mining a klasifikační modely. Hradec Králové: Gaudeamus, 2010. ISBN: 978-80-7435-088-7. |
| Recommended Reading: |
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AGGRAWAL, Charu. Data Mining: The Textbook. Cham: Springer, 2015. ISBN 978-3-319-14141-1.
BRAMER, Max. Principles of data mining. London: Springer-Verlag, 2013. ISBN 978-1-4471-4884-5.
HUDEC, Miroslav. Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Cham: Springer, 2016. ISBN 978-3-319-42516-0. |
AGGRAWAL, Charu. Data Mining: The Textbook. Cham: Springer, 2015. ISBN 978-3-319-14141-1.
BRAMER, Max. Principles of data mining. London: Springer-Verlag, 2013. ISBN 978-1-4471-4884-5.
HUDEC, Miroslav. Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Cham: Springer, 2016. ISBN 978-3-319-42516-0.
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| Planned learning activities and teaching methods |
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| Lectures, Individual consultations, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Credit and Examination | Credit and Examination | 100 (100) | 51 |
| Credit | Credit | 40 (40) | 20 |
| Written test | Written test | 15 | 5 |
| project | Project | 25 | 10 |
| Examination | Examination | 60 | 31 |