Mining and Visualisation of Knowledge

Lecturer:

Olga Štěpánková (gar.), Lenka Vysloužilová

Tutor:

Olga Štěpánková (gar.), Tomáš Sieger, Tibor Strašrybka, Lenka Vysloužilová

Supervisor:

Department of Cybernetics

Synopsis:

The subject reviews current tools for data mining and illustrates their properties using real-life tasks. Specific attention is devoted to descriptive presentation of the obtained results along the data-mining process - an approach that significantly improves and facilitates communication with the domain expert or data owner (e.g. medical professional) who can thus take active part in the process by focussing to the most promising direction.

Requirements:

Syllabus of lectures:

1. Data-mining - CRISP-DM process description and methodology. Some motivating case studies.

2. Review of data modelling tools and examples of their application I.

3. Sources of data. Data anonymization and protection.

4. Fusing data from heterogeneous sources.

5. Data understanding, pre-processing and aggregation.

6. Methods of data visualisation. Identification of outliers or wrong values.

7. Choice of relevant attributes.

8. Time series data and their processing.

9. Review of data modelling tools and examples of their application II.

10. Model evaluation and knowledge derived. Deployment.

11. Visualization of models

12. Processing input in the form of natural language text.

13. Processing very complex data.

14. Reserve.

Syllabus of tutorials:

Accompanying computer labs provide the students with an opportunity to master the tools and methods presented during the lectures when solving some simple real-life problems. Hand-on exercises follow the syllabus of the lecture. All the students are given individual data mining assignments which help them to gain experience in CRISP data mining methodology.

Study Objective:

Study materials:

[1] Few, S.: Simple Visualization Techniques for Quantitative Analysis - Now you see it. Analytics Press 2009.

[2] Larose, D.T.: Discovering Knowledge in Data: An Introduction to Data Mining, Wiley 2005.

[3] Larose, D.T.: Data Mining - Methods and Models, Wiley 2006.

Note:

Time-table for winter semester 2011/2012:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Fri
Thu	roomKN:E-220 Vysloužilová L. 09:15–10:45 (lecture parallel1 parallel nr.102) Karlovo nám. Laboratoř BIOroomKN:311 Štěpánková O. Vysloužilová L. 11:00–12:30 (lecture parallel1) Karlovo nám. STROJNÍ FakroomKN:E-220 Sieger T. 12:45–14:15 (lecture parallel1 parallel nr.101) Karlovo nám. Laboratoř BIOroomKN:E-220 Strašrybka T. 14:30–16:00 (lecture parallel1 parallel nr.103) Karlovo nám. Laboratoř BIO
Fri

Time-table for summer semester 2011/2012:

Time-table is not available yet

The course is a part of the following study plans:

• Biomedicínské inženýrství a informatika - Biomedicínské inženýrství (compulsory course of the specialization)