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

To familiarize students with the methods and algorithms of data modelling for knowledge discovery from it.

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

• Knowledge of the basic steps of the data mining process and methods of data preparation for the step of data modelling (discussed in the subject UPA - Data Storage and Preparation).
• Basic knowledge of probability and statistics.
• Knowledge of database technology at a bachelor subject level. 

• Skiena, S.S.: The Data Science Design Manual. Springer, 2017, 445 p. ISBN 978-3-319-55443-3.
• Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Third Edition. Morgan Kaufmann Publishers, 2012, 703 p., ISBN 978-0-12-381479-1.
• Bishop, C.M: Pattern Recognition and Machine Learning. Springer, 2006, 738 p. ISBN 0387310738.
• Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Second Edition. Elsevier Inc., 2006, 770 p., ISBN 1-55860-901-3.
• Zendulka, J. a kol.: Získávání znalostí z databází. FIT VUT v Brně, 160 s., 2009. (elektronicky)

• 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. Data Warehouse and OLAP Technology for knowledge discovery.
2. Mining frequent patterns and associations - basic concepts, efficient and scalable frequent itemset mining methods.
3. Multi-level association rules, association mining and correlation analysis, constraint-based association rules.
4. Predictive modelling - basic concepts, classification methods - decision tree, Bayesian classification, rule-based classification.
5. Classification by means of neural networks, SVM classifier, Random forests.
6. Other classification and regression methods. Evaluation of quality of classification and regression.
   Cluster analysis - basic concepts, types of data in cluster analysis.
7. Partitioning-based and hierarchical clustering. Other clustering methods. Evaluation of quality of clustering.
8. Outlier analysis. Mining in biological data.
9. Introduction to mining data stream and time-series.
10. Introduction to mining in sequences, graphs, spatio-temporal data, moving object data and multimédia data. 
11. Text mining.
12. Mining the Web. Process mining.
13. Other selected topics (Process Mining, Recommender Systems, Big data analytics).

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

• A mid-term test - 15 points
• Formulation of a data mining task - 5 points
• Presentation of the project - 29 points
• Final exam - 51 points
• To be allowed to sit for written examination student is to present and defend project oucomes in due dates, and to earn at least 24 points during the semester.
• The minimum number of points for the final examination is 20.

• Mid-term written exam, there is no resit, excused absences are solved by the guarantor.
• The formulation of the data mining task in the prescribed term, excused absences are solved by the assistent.
• The presentation of the project results in the prescribed term, excused absences are solved by the assistent.
• Final exam, The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to the student. Excused absences are solved by the guarantor.