Course for international guest/part time students
- Faculty
- Faculty of Science
- Organization
- TTK Department of Computer Science
- Code
- adatba1u0um17gm
- Title
- Data mining (p)
- Usual semester
- Spring
- ECTS
- 3
- Language
- Learning outcomes
- Knowledge: getting familiar with the main notions of data mining Ability: to understand and use the mathematical models of data mining Attitude: the need to deepen the applied mathematical knowledge, to gain new applied mathematical skills, to develop competencies. Aspiration to apply the mathematical knowledge for a wide range of problems Autonomy and Responsibility: based on the gained knowledge in data mining, the students are able to decide which tools are the most suitable to solve applied problems
- Course content
- Dimension reduction procedures. Spectral methods, approximation with a low rank matrix. Fingerprints, fingerprint-based similarity search. Selection of variables. Classification. Decision trees, k-NN, neural networks, Bayesian methods. Kernel method, SVM, hybrid methods. Clustering. Partitioning algorithms, k-means. Hierarchical algorithms. Density and link-based methods, DBSCAN, OPTICS. Spectral clustering. Applications and implementation issues. Data mining system architectures. Data structures
- Assessment method
- term grade
- Recommended bibliography
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison-Wesley, 2006, ISBN 0321321367 Jiawei Han és Micheline Kamber: Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers, 2006, ISBN 1558609016