Knowledge Discovery in Databases

• 39 hrs lectures
• 13 hrs projects

• 51 pts final exam (written part)
• 15 pts mid-term test (written part)
• 34 pts projects

• Students get a broad, yet in-depth overview of the field of data mining and knowledge discovery.
• They are able both to use and to develop knowledge discovery tools.

• Student learns terminology in Czech ane English language.
• Student gains experience in solving projects in a small team.
• Student improves his ability to present and defend the results of projects.

To familiarize students with knowledge discovery in data sources, to explain useful knowledge types and the steps of the knowledge discovery process, and to familiarize them with techniques, algorithms and tools used in the process.

Due to the increasing amounts of data currently stored in databases, it is necessary to discover some new knowledge, which is not possible to obtain with use of querying the data. Therefore, it is necessary to become acquainted with the whole process of knowledge discovery, which includes data pre-processing and data mining algorithms, where methods from various areas, such as statistics and machine learning, is used. 

• Basic knowledge of probability and statistics.
• Knowledge of database technology at a bachelor subject level. 

• Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.

• Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
• Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.  

1. Introduction - motivation, fundamental concepts, data source and knowledge types.
2. Data Preparation - characteristics of data.
3. Data Preparation - methods.
4. Data Warehouse and OLAP Technology for knowledge discovery.
5. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
6. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
7. Classification and prediction - basic concepts, decision tree, Bayesian classification, rule-based classification.
8. Classification by means of neural networks, SVM classifier, other classification methods, prediction.
9. Cluster analysis - basic concepts, types of data in cluster analysis, partitioning and hierarchical methods.
10. Other clustering methods. Mining in biological data.
11. Introduction to mining data stream, time-series and sequence data.
12. Introduction to mining in graphs, spatio-temporal data, moving object data and multimédia data. 
13. Text mining, mining the Web.

• Working-out a data mining project by means of an available data mining tool.

A mid-term test, formulation of a data mining task, presentation of the project.
Exam prerequisites:
Duty credit consists of working-out the project, defending project results and of obtaining at least 24 points for activities during semester.

A mid-term test, formulation of a data mining task, presentation of the project. The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to the student.

Duty credit consists of working-out the project, defending project results and of obtaining at least 24 points for activities during semester.