SGN-5306 KNOWLEDGE MINING, Period I, 2012, 3cr.

 

 

Professori Ari Visa,  ari.visa@tut.fi

Room TF309

Phone 3115 4388

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Lectures 24 h.

 

 

Time and Place:                     The Course will be lectured during period I. Schedule is available at the end of this page. The first meeting will take place on the Monday, 27st of  August, 10:15 a.m. in the lecture room TB224.

 

Topics:                                    By the increasing popularity of the Internet and large databases the need to knowledge retrieval and management is increasing.   On the course the following topics will be treated: data preprocessing, decision trees, rule based reasoning, cluster analysis, incremental learning,  and so on. The lecture plan is available at the end of this page. The original lecture slides are available at the address http://www.cs.sfu.ca/~han/dmbook . The aim is to introduce the main approaches in knowledge mining and to create the capability to use and  to develop the presented methods.

 

Audience:                               The course is intended to students who are close to graduation in the fields of signal processing, computer science or telecommunication. The course is also suitable to post-graduate studies.

 

Requirements:                       The examination is based on the final exam and an exercise work.

                                                M.Sc. Marja  Ruotsalainen takes care of exercise work, Email marja.ruotsalainen@tut.fi . The exercise work is available at the address http://www.cs.tut.fi/~merta/KM.html .

 

 

Literature:                             Data Mining: Concepts and Techniques,  Jiawei Han, Micheline Kamber, Morgan Kaufmann Publisher, 2000 (DMCT).

                                                Principles of Data Mining, David, J., Hand, Heikki Mannila, Padhric Smyth, MIT Press 2000 (PDM).

 

 

 

Date

Place

Subject

 

27.8.2012

TB224

1. Introduction  Chapter 1 DMCT

http://www.cs.tut.fi/~avisa/5306lec1.pdf

30.8.2012

TB223

2. Data Warehousing and OLAP technology for data mining  Chapter 2 DMCT

http://www.cs.tut.fi/~avisa/5306lec2.pdf

3.9.2012

TB224

3. Data Preprocessing    Chapter 3 DMCT,  Models and Patterns  Chapter 6 PDM

http://www.cs.tut.fi/~avisa/5306lec3.pdf

6.9.2012

TB223

4. Data mining primitives, languages and system architectures   Chapter 4 DMCT

http://www.cs.tut.fi/~avisa/5306lec4.pdf

10.9.2012

TB224

5. Concept description: Characterization and Comparison  Chapter 5 DMCT,

Descriptive Modeling   Chapter 9 PDM

http://www.cs.tut.fi/~avisa/5306lec5.pdf

13.9.2012

TB223

6. Mining association rules in large databases  Chapter 6

http://www.cs.tut.fi/~avisa/5306lec6.pdf

17.9.2012

TB224

7. Classification and prediction   Chapter 7 DMCT

http://www.cs.tut.fi/~avisa/5306lec7.pdf

24.9.2012

TB224

8.  Predictive Modeling for Classification   Chapter 10 PDM

 

27.9.2012

TB223

9. Clustering analysis   Chapter 8 DMCT

http://www.cs.tut.fi/~avisa/5306lec8.pdf

1.10.2012

TB224

10. Clustering analysis + demands for the exam  Chapters 11 PDM

 

4.10.2012

TB223

11. Mining complex types of data   Chapter 9 DMCT

http://www.cs.tut.fi/~avisa/5306lec9.pdf

8.10.2012

TB224

12. Data Mining applications and trends in data mining   Chapter 10 DMCT

http://www.cs.tut.fi/~avisa/5306lec10.pdf