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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Mining and Visualisation of Knowledge

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Code Completion Credits Range Language
A6M33DVZ Z,ZK 4 2+2c Czech
Lecturer:
Olga Štěpánková (guarantor), Tomáš Sieger
Tutor:
Olga Štěpánková (guarantor), Milan Němý, 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:
Further information:
http://cw.felk.cvut.cz/doku.php/courses/a6m33dvz/start
Time-table for winter semester 2018/2019:
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-112
Štěpánková O.
Sieger T.

11:00–12:30
(lecture parallel1)
Karlovo nám.
Cvičebna Vyčichlova
roomKN:E-230
Němý M.
Vysloužilová L.

12:45–14:15
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Fri
Time-table for summer semester 2018/2019:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-05-21
For updated information see http://bilakniha.cvut.cz/en/predmet1707606.html