Entry requirements

120 credits with 25 credits in mathematics, including mathematical statistics, 45 credits in computer science and/or engineering, including Database Design I and a second course in computer programming. Algorithms and Data Structures I is recommended. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course the student shall be able to:

• explain different methods to extract processed information from large amounts of data, both in theory and in practical application
• use these methods with appropriate tools
• evaluate and compare the suitability of different methods
• make judgments with regard to relevant scientific, social and ethical aspects in the application of data mining
• solve data mining problems in a team.

Content

Introduction to data mining and its terminology, overview over various types of data and their properties, data preprocessing, classification methods (prototype-based, hierarchical, and density-based clustering algorithms, validation), association analysis (Apriori, FP-growth, sequential patterns), outlier detection, social and ethical aspects in the area of data mining.

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

Instruction

Lectures, seminars, laboratory sessions and project.

Assessment

Written examination and a project that is presented in writing.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.