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Lecturer(s)
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Viktorin Adam, Ing. Ph.D.
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Šenkeřík Roman, prof. Ing. Ph.D.
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Course content
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- Introduction to data mining - history, principles and procedures, applications. - Dimensionality reduction - PCA algorithm. feature extraction and feature selection. Ranking algorithms - PageRank. - Clustering algorithms - K-means, Fuzzy cMeans and others. - DBSCAN, EM algorithm. - Heuristic analysis. - Data mining from time series. - Data streams and Big Data mining. - Mining association patterns. - Agent systems - theories and their applications. - Multiagent systems - theories and their applications. - Multiagent systems in cyber security.
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Learning activities and teaching methods
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Lecturing, Exercises on PC
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| prerequisite |
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| Knowledge |
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| Knowledge from areas: Mathematics Fundamentals of Informatics Programming Fundamentals of artificial intelligence |
| Knowledge from areas: Mathematics Fundamentals of Informatics Programming Fundamentals of artificial intelligence |
| learning outcomes |
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| The student can enumerate and describe the principles, and procedures of data mining and the basic classification of methods and algorithms in soft computing. |
| The student can enumerate and describe the principles, and procedures of data mining and the basic classification of methods and algorithms in soft computing. |
| The student understands methods for dimensionality reduction, such as PCA, and knows the principles of clustering algorithms, including K-means and DBSCAN. |
| The student understands methods for dimensionality reduction, such as PCA, and knows the principles of clustering algorithms, including K-means and DBSCAN. |
| The student has knowledge of techniques for mining data from time series and processing Big Data. |
| The student has knowledge of techniques for mining data from time series and processing Big Data. |
| The student understands the theory and applications of agent and multi-agent systems, especially in the context of cybersecurity. |
| The student understands the theory and applications of agent and multi-agent systems, especially in the context of cybersecurity. |
| The student has knowledge of methods for mining associative patterns and the basics of heuristic analysis. |
| The student has knowledge of methods for mining associative patterns and the basics of heuristic analysis. |
| Skills |
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| The student can implement algorithms for dimensionality reduction and pre-proscessing for effective data processing and analysis. |
| The student can implement algorithms for dimensionality reduction and pre-proscessing for effective data processing and analysis. |
| The student can use clustering algorithms for data analysis. |
| The student can use clustering algorithms for data analysis. |
| The student is capable of analyzing and processing data from time series and Big Data for specific needs in cybersecurity. |
| The student is capable of analyzing and processing data from time series and Big Data for specific needs in cybersecurity. |
| The student has skills in developing and applying agent systems to solve security problems. |
| The student has skills in developing and applying agent systems to solve security problems. |
| The student can utilize data analysis to identify and solve complex problems in cybersecurity. |
| The student can utilize data analysis to identify and solve complex problems in cybersecurity. |
| teaching methods |
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| Knowledge |
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| Exercises on PC |
| Lecturing |
| Lecturing |
| Exercises on PC |
| assessment methods |
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| Oral examination |
| Oral examination |
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Recommended literature
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AGGARWAL, Charu C. Data mining: the textbook. 2015. ISBN 978-3-319-14141-1.
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Aliev, R. A. Soft computing and its applications. Singapore : World Scientific, 2001. ISBN 981-02-4700-1.
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Burian P. Webové a agentové technologie. Praha, 2012. ISBN 978-80-247-4376-9.
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Ferber, Jacques. Multi-agent systems : an introduction to distributed artificial intelligence. 1st ed. Harlow : Addison Wesley, 1999. ISBN 201360489.
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HAND D., MANNILA H., SMYTH P. Principles of Data Mining. Cambridge : MIT Press, 2001. ISBN 026208290X.
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IGNATOW G., MIHALCEA R. An introduction to text mining: research design, data collection, and analysis. 2018. ISBN 978-1-5063-3700-5.
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Kacprzyk J, Pedrycz, W. Springer handbook of computational intelligence. 2015.
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MAIMON, O., ROKACH, L. Data Mining and Knowledge Discovery Handbook. Hardcover. ISBN 978-0387244358.
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MARZ, Nathan a James WARREN. Big data: principles and best practices of scalable real-time data systems.. New York, 2015. ISBN 978-1-61729-034-3.
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ROKACH L., MAIMON O.Z. Data mining with decision trees: theory and applications. Second edition. 2015. ISBN 978-981-4590-07-5.
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Volná E. Základy soft computingu. Ostravská Univerzita, 2012.
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WITTEN, I. H. Data mining: practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, 2017. ISBN 9780128042915.
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