Entry requirements

120 credits including Data Mining I and Database Design II.

Learning outcomes

To pass, the student should be able to

• explain advanced methods for data mining from large amounts of data, both in theory and in practical application
• implement and use these methods with appropriate tools and
• evaluate and compare the suitability, scalability and efficiency of different methods.

Content

Advanced methods for classification, clustering and association analysis. Sequential patterns, web-based data and search engines, outlier analysis, stream-based data mining and methods for integrity preserving data mining.

The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical data sets.

Instruction

Lectures, seminars, laboratory sessions and written assignments. Guest lecture

Assessment

Written examination (3 HE credits) and assignments that are presented orally and/or written (2 HE credits).

Other directives

The course can not be counted in higher education qualification together with the course Data Mining (7.5 HE credits).