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 in data mining covering the fields of web data mining and search engines, spatial and temporal data mining including cluster evaluation and advanced clustering, stream based data mining, and sequential association analysis. Additional areas include advanced association analysis, methods for large-scale data mining, introduction to outlier analysis, and methods for privacy-preserving in data mining.

Instruction

Lectures, seminars, laboratory sessions and assignments. Guest lecture

Assessment

Written examination (3 HE credits) and written and oral presentation of assignments (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).