x !
Archived Curricula Guide 2008–2010
Curricula Guide is archieved. Please refer to current Curricula Guides
TKOPS126 Data Mining 10 ECTS
Organised by
Computer Science
Preceding studies
Compulsory:
It is recommended that students have completed the obligatory basic courses in Mathematics before taking this course.

Learning outcomes

The student learns the premises, objectives and relevance as well as the basic methods of data mining.

Contents

The relevance and definitions of data and data measurement, visualisation and analysis of data, unstructured and incomplete data, data mining algorithms, models and patterns, scoring functions of data mining algorithms, searching and optimisation methods, descriptive modelling, predictive modelling of classification, data management and databases in conjunction with data mining, searching for patterns and rules, and searching on the basis of contents.

Teaching methods

Teaching method Contact Online
Lectures 48 h 0 h
Exercises 20 h 0 h

Teaching language

Finnish

Modes of study

Written examination and completed weekly exercises.

Evaluation

Numeric 1-5.

Recommended year of study

Advanced studies, 2nd year, spring semester or later.
The course is not lectured every year.

Study materials

  1. Hand D., Mannila H. & Smyth P., Principles of Data Mining. MIT Press 2001.

Belongs to following study modules

Department of Computer Sciences
1.2 Optional advanced courses in computer science (Computer Science)
Department of Computer Sciences
1.1 Compulsory advanced courses in information management (Computer Science)
2009–2010
Teaching
Archived Teaching Schedule. Please refer to current Teaching Shedule.
-
Department of Computer Sciences