Course: Data mining

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Course title Data mining
Course code KGI/DAMIN
Organizational form of instruction Lecture + Exercise
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 10
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Dvorský Jiří, doc. Mgr. Ph.D.
  • Dobešová Zdena, doc. Ing. Ph.D.
Course content
The course provides basic information in the areas of data mining, data analysis, neural networks and bioinspirovaných calculations. Presented theoretical knowledge and procedures are the basis for independent experiments the students with the analysis and data processing, possibly with the implementation of selected methods. Syllabus: 1. Defining the problem of Data Mining. 2. Input data, data types from the formal and semantic. Coarse filtration. Missing data, dichotomization, categorization. 3. Using the methods of linear algebra (PCA) 4. The survey of similarities and dissimilarities objects - coefficients of association and metrics 5. Cluster analysis, non-hierarchical methods, hierarchical methods, presentation and interpretation of results. 6. Evolutionary Computation. Basic concepts: individual, population, fitness, objective function, representation of individuals. Objective function, design principles, function testing, computational complexity of algorithms and theoretical limits 7. Selected stochastic algorithms method local search algorithm blind, climbing algorithm, simulated annealing. 8. Ant Colony Optimization (Ant Colony Optimization) 9. Models neurons. Neuron 1st generation. Neurons 2nd generation - Perceptron. Perceptron adaptation. 10. Multi-layer topology. Backpropagation method. 11. Kohonen learning and self-organizing neural network SOM. Genetic algorithms in neural networks adapt. 12. Decision trees, association rules 13. Visual Programming Languages in GIS

Learning activities and teaching methods
Monologic Lecture(Interpretation, Training)
Learning outcomes
The aim of the course is to acquaint with information on data mining, data analysis, neural networks and bio inspired algorithms.
Students will understand the techniques of data mining and data analysis.
Prerequisites
Basic knowledge of statistics, geostatistics and GIS.

Assessment methods and criteria
Written exam, Seminar Work

Theoretical and practical knowledge of presented topics.
Recommended literature
  • Berka P. (2003). Dobývání znalostí z databází. Praha.
  • Gurney Kevin. (2009). An Introduction to Neural Networks. UCL Press.
  • Jiawei Han, Micheline Kamber, Jian Pei. (2011). Data Mining: Concepts and Techniques: Concepts and Techniques. Morgan Kaufmann.
  • Marco Dorigo, Thomas Stutzle. (2004). Ant Colony Optimization. The MIT Press.
  • Petr. P. (2014). Metody Data Miningu (část I). Pardubice, Univerzita Pardubice.
  • Petr., P. (2014). Metody Data Miningu (část 2). Pardubice, Univerzita Pardubice.
  • Šarmanová, J. (2012). Metody analýzy data. Ostrava.
  • Yang Xiao, Fei Hu. (2010). Bio-inspired Computing and Communication Networks. CRC.
  • Yuehui Chen, Ajith Abraham. (2009). Tree-Structure based Hybrid Computational Intelligence: Theoretical Foundations and Applications. Springer .


Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester
Faculty of Science Geoinformatics (1) Geography courses 1 Winter