Data Mining and Knowledge Discovery

Lectures:

18 h

Recommended for:

Graduate students.

The course last ran:

Spring 1999.

Goals:

To overview methodologies suitable for the data exploration supported by the examples.

Prerequisites:

None.

Organization:

Mainly lectures, seminar presentations in addition.

Contents:

Quantitative and qualitative data analyses. Relating expectations of the user domains to a corresponding level of information/knowledge engineering. Addressing questions of time granularities. Estimating the scope and performance of data mining and knowledge discovery.

Literature:

Compendium.

Teachers:

Ankica Babic.

Examiner:

Active participation, seminar presentations.

Schedule:

Spring 2002.

Examination:

Ankica Babic.

Credit:

3 (+2) credits.