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Lecturer(s)
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Mirshahi Sina, MSc.
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Šenkeřík Roman, prof. Ing. Ph.D.
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Sahgal Divya
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Komínková Oplatková Zuzana, 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. - Data mining from time series. - Data streams mining. - Mining association patterns. - Mining of discrete sequences. - Big data mining. - Statistical learning, naive Bayesian classifier, Bayesian networks. - Support vector machines. - Decision trees. Random forest. - Multicriteria decision analysis.
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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 delineate and describe the principles, and procedures of data mining, including its applications. |
| The student can delineate and describe the principles, and procedures of data mining, including its applications. |
| The student understands methods of dimensionality reduction, such as PCA, and knows the principles of clustering algorithms, for example, K-means and Fuzzy cMeans. |
| The student understands methods of dimensionality reduction, such as PCA, and knows the principles of clustering algorithms, for example, K-means and Fuzzy cMeans. |
| The student has knowledge about techniques for mining data from time series and data streams. |
| The student has knowledge about techniques for mining data from time series and data streams. |
| The student understands methods for mining associative patterns and discrete sequences. |
| The student understands methods for mining associative patterns and discrete sequences. |
| The student has knowledge of the fundamental principles of machine learning, including decision trees, naive Bayesian classifiers and Bayesian networks. |
| The student has knowledge of the fundamental principles of machine learning, including decision trees, naive Bayesian classifiers and Bayesian networks. |
| Skills |
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| The student can apply dimensionality reduction algorithms, such as PCA, for processing and analyzing data. |
| The student can apply dimensionality reduction algorithms, such as PCA, for processing and analyzing data. |
| The student can implement and use various clustering algorithms for group data analysis. |
| The student can implement and use various clustering algorithms for group data analysis. |
| The student is capable of developing solutions for efficient mining of data from time series and data streams. |
| The student is capable of developing solutions for efficient mining of data from time series and data streams. |
| The student has skills in analyzing and creating association rules to uncover patterns in data. |
| The student has skills in analyzing and creating association rules to uncover patterns in data. |
| The student can utilize machine learning methods, such as decision trees and support vector machines, for data analysis and prediction. |
| The student can utilize machine learning methods, such as decision trees and support vector machines, for data analysis and prediction. |
| teaching methods |
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| Knowledge |
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| Exercises on PC |
| Exercises on PC |
| Lecturing |
| Lecturing |
| 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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Brink H., Richards J.W., Fetherolf M. Real-world machine learning. 2017. ISBN 978-1-61729-192-0.
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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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LAM H-K., LING S. H., NGUYEN H. T. Computational intelligence and its applications: evolutionary computation, fuzzy logic, neural network and support vector machine techniques. 2012. ISBN 978-1-84816-691-2.
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MAIMON, O., ROKACH, L. Data Mining and Knowledge Discovery Handbook. Hardcover. ISBN 978-0387244358.
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Novák, V. Fuzzy množiny a jejich aplikace. Praha : SNTL, 1990. ISBN 80-03-00325-3.
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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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