Knowledge Discovery in Databases
ZZD Acad. year 2007/2008 Winter semester
- The deepening of basics in KDD - basics of methods of data preprocessing (statistics quantities used in data summarization, approaches to data cleaning, transformation and reduction), basics of data warehousing, basic methods and algorithms of mining frequent items and patterns and association rules (Apriori algorithm, FP-tree, multi-level association rules, mining multidimensional association rules from relational databases), basic methods and algorithms of classification (decision tree, Bayesian classification, using neural networks, SVM) and prediction (linear and nonlinear regression), basic methods and algorithms of cluster analysis (distance of data, partitioning methods, hierarchical methods, CF-tree, density-based methods, grid- and model-based methods).
- Advanced data mining techniques - advanced techniques of data mining in 'classic' data sources, mining in data streams, time series and sequences, mining in biological data; mining in graphs, multirelational data mining, mining in object, spatial and multimedia data, mining in text, mining on the Web.
Language of instruction
Subject specific learning outcomes and competences
Prerequisite kwnowledge and skills
- Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice Hall, 2002, 336 p Fayyad U.M. (Ed.): Advances in Knowledge Discovery and Data Mining. AAAI Press/the MIT Press, 1996, 560 p.
- Aggarwal, Ch.C. (ed.): Data Streams: Models and Algorithms. Advances in Database Systems. Springer, 2006, 358 p., ISBN 0387287590.
- Wang, J. (ed.): Encyclopedia of Data Warehousing and Mining, Hershey, US, IDEA, 2005, 1382 p., ISBN 1-59140-557-2.
- Papers in journals and conference proceedings (including those in ACM Digital library, IEEE Digital library and other electronic sources).
- Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
Syllabus of lectures
- Data preprocessing.
- Data warehousing.
- Asociation analysis.
- Classification and prediction.
- Cluster analysis.
- Advanced data mining in 'classic' data sources.
- Mining in data streams.
- Data mining in time series and sequences.
- Mining in biological data.
- Data mining in graph structures.
- Multirelational data mining.
- Mining in object, spatial and multimedia data.
- Text mining and Web mining.
Syllabus - others, projects and individual work of students
- Reading up and treatment of a selected topic concerning knowledge discovery in a field related to the student's PhD thesis.
Course inclusion in study plans