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Lecturer(s)
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Dobešová Zdena, doc. Ing. Ph.D.
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Pavlačka Daniel, Mgr.
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Course content
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1. Definition of problems of multivariate data analysis 2. Input data, data types, coarse filtration, missing data, dichotomization, categorization 3. Principal component analysis, correspondence analysis 4. Detection of similarities and dissimilarities of objects - coefficients of association and metrics 5. Classification methods, clustering, the complexity of algorithms 6. Decision trees 7. Association rules, frequent itemsets, spatial association rules 8. Grouping, neural networks - SOM 9. Visual Programming in GIS 10. Time series processing - decomposition 11. Time series processing - forecasting
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training)
- Attendace
- 78 hours per semester
- Homework for Teaching
- 78 hours per semester
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Learning outcomes
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The course provides basic information in the areas of data preparation, data mining, data analysis and interpretation of results. The presented theoretical knowledge and procedures will be the basis for independent experiments of students with knowledge discovery in databases.
Apply the knowledge for solution of the complex analytical problem, elaboration of the problem according to the chosen and/or given topic.
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Prerequisites
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Basic knowledge of statistical methods and work with data.
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Assessment methods and criteria
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Oral exam, Seminar Work, Written exam
Theoretical and practical knowledge of presented topics.
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Recommended literature
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ČSÚ. (2020). Česko v číslech. Praha.
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Dobesova, Z. (2019). Discovering association rules of information dissemination about Geoinformatics university study. Springer.
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Dobesova Z. (2020). PD-28 Visual Programming for GIS Applications, Geographic Information Science & Technology Body of Knowledge BoK. Ithaca, New York.
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Dobesova, Z., Pinos J. (2019). Using decision trees to predict the likelihood of high school students enrolling for university studies. , Advances in Intelligent Systems and Computing.
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Hančlová, J., Tvrdý, L. (2003). Úvod do analýzy časových řad. Ekonomická fakulta VŠB-TU Ostrava.
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Křivý I. (2012). Analýza časových řad. Ostrava.
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Litschmannová M. (2010). Úvod do analýzy časových řad. VŠB -TU Ostrava.
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Marek, L. (2015). Prostorové a vícerozměrné statistické analýzy epidemiologických dat. Univerzita Palackého, Olomouc, doktorská práce.
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Petr P. (2014). Data Mining (1. a 2. část). Univerzita Pardubice.
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Sankey T. (2017). Statistical Descriptions of Spatial Patterns, In: Shekhar S., Xiong H., Zhou X. (eds) Encyclopedia of GIS. Springer.
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Šarmanová, J. (2012). Metody analýzy data. Ostrava.
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Witten IH, Frank F, Hall AH. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
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