Data Mining

Lecturer:

Pavel Kordík (gar.)

Tutor:

Pavel Kordík (gar.)

Supervisor:

Department of Computer Science

Synopsis:

Students are introduced to the basic methods of discovering knowledge in data. In particular, they learn the basic techniques of data preprocessing, multidimensional data visualization, statistical techniques of data transformation, and fundamental principles of knowledge discovery methods. Students will be aware of the relationships between model bias and variance, and know the fundamentals of assessing model quality. Data mining software is extensively used in the module. Students will be able to apply basic data mining tools to common problems (classification, regression, clustering).

Requirements:

Syllabus of lectures:

1. Introduction to data mining, data preparation, data visualization.

2. Statistical analysis of data.

3. Data model, nearest neighbour classifier.

4. Training, validation and testing, model's quality evaluation.

5. Artificial neural networks in data mining.

6. Unsupervised neural networks - competitive learning

7. Probability and Bayesian classification.

8. Decision trees and rules.

9. Neural networks with supervised learning.

10. Cluster analysis.

11. Combining neural networks and models in general.

12. Data mining in the Clementine environment.

13. Text mining, Web mining, selected applications, new trends.

Syllabus of tutorials:

1. Data, visualization, statistics.

2. Statistical analysis of data.

3. Data preprocessing, dimension reduction, relevance of inputs.

4. Model, learning, testing, model validation.

5. Data mining process, classification, prediction, modeling.

6. Cluster analysis, SOM.

7. Project assignment

8. [3] Consultations, working on projects.

9. [3] Presentations of results, workshop.

10. Assessment.

Study Objective:

The module aims to introduce students to a rapidly developing field - knowledge discovery in data.

Study materials:

1. Larose, D. T. Discovering Knowledge in Data: An Introduction to Data Mining. Wiley-Interscience, 2004. ISBN 0471666572.

Note:

Time-table for winter semester 2011/2012:

Time-table is not available yet

Time-table for summer 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	roomT9:349 Kordík P. 09:15–10:45 (lecture parallel1) Dejvice NBFIT místnostroomT9:351 Kordík P. 11:00–12:30 (lecture parallel1 parallel nr.101) Dejvice NBFIT PC ucebnaroomT9:351 Kordík P. 12:45–14:15 (lecture parallel1 parallel nr.102) Dejvice NBFIT PC ucebna
Tue
Fri
Thu
Fri

The course is a part of the following study plans:

• Computer Science, Version for Students who Enrolled in 2009 and 2010, Presented in Czech (compulsory course of the specialization)
• Information Systems and Management, Version for Students who Enrolled in 2009 and 2010, in Czech (VO)
• Informatics, Version for Students who Enrolled in 2009 and 2010, Presented in Czech (VO)
• Informatics (Bachelor)- Version for those who Enrolled in 2011 and 2012 (in Czech) (VO)
• Information Systems and Management - Version for those who Enrolled in 2011 and 2012 (in Czech) (VO)
• Computer Science - Version for those who Enrolled in 2011 and 2012 (in Czech) (compulsory course of the specialization)