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.
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.