Data Mining - Clustering and Association Analysis, 6 credits (TDDD41)

Data Mining - Clustering and Association Analysis, 6 hp

Main field of study

Information Technology Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Patrick Lambrix

Director of studies or equivalent

Patrick Lambrix

Available for exchange students

Yes
Course offered for Semester Period Timetable module Language Campus VOF
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 1 3 English Linköping v
6MDAV Computer Science, Master's programme 2 (Spring 2017) 1 3 English Linköping v
6CITE Information Technology, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 1 3 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 1 3 English Linköping v
6CIII Industrial Engineering and Management, M Sc in Engineering (Specialization Computer Science and Engineering) 8 (Spring 2017) 1 3 English Linköping v
6CIII Industrial Engineering and Management, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering (Specialization Computer Science and Engineering) 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering (Specialization Computer Science and Engineering) 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering (Specialization Computer Science and Engineering) 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering (Specialization Computer Science and Engineering) 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering (Specialization Computer Science and Engineering) 8 (Spring 2017) 1 3 English Linköping v
6CIEI Industrial Engineering and Management - International, M Sc in Engineering 8 (Spring 2017) 1 3 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (Programming and Algorithms) 8 (Spring 2017) 1 3 English Linköping v
6CITE Information Technology, M Sc in Engineering (Programming and Algorithms) 8 (Spring 2017) 1 3 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (Programming and Algorithms Specialization) 8 (Spring 2017) 1 3 English Linköping v
6MICS Computer Science, Master's programme 2 (Spring 2017) 1 3 English Linköping v

Main field of study

Information Technology, Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Computer Science and Engineering, M Sc in Engineering
  • Computer Science, Master's programme
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Industrial Engineering and Management, M Sc in Engineering
  • Industrial Engineering and Management - International, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

The course requires thorough knowledge in programming, discrete mathematics, data structures and algorithms and databases.

Intended learning outcomes

The course lays the foundation for professional work and research in which large amounts of data are explored, modified, modelled and assessed to uncover previously unknown patterns and trends. The course focuses on clustering and association analysis.
Having completed the course, the student should be able to:

  • understand and be able to use important terminology in data mining
  • understand and use the theory behind clustering and association analysis
  • use knowledge about techniques for clustering and association analysis
  • demonstrate insightful assessment of the quality of given data sets and the information content on which clustering and association analysis can be based
  • use and evaluate tools for clustering and association analysis

 

Course content

Association analysis: concepts and methods related to frequent item sets and association rules such as Apriori principle, FP-growth, evaluation of association rules,

Clustering: concepts and methods related to partitional clustering methods, hierarchical clustering methods, density-based clustering methods, cluster evaluation

Teaching and working methods

The teaching comprises lectures and computer laboratory work. Lectures are devoted to theory, concepts and techniques. The techniques are practised in the computer laboratory work.

Examination

TEN1Written examinationU, 3, 4, 54 credits
LAB1Laboratory workU, G2 credits

Grades

Four-grade scale, LiU, U, 3, 4, 5

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Patrick Lambrix

Examiner

Patrick Lambrix

Course website and other links

http://www.ida.liu.se/~TDDD41

Education components

Preliminary scheduled hours: 26 h
Recommended self-study hours: 134 h
There is no course literature available for this course.
TEN1 Written examination U, 3, 4, 5 4 credits
LAB1 Laboratory work U, G 2 credits

Regulations (apply to LiU in its entirety)

The university is a government agency whose operations are regulated by legislation and ordinances, which include the Higher Education Act and the Higher Education Ordinance. In addition to legislation and ordinances, operations are subject to several policy documents. The Linköping University rule book collects currently valid decisions of a regulatory nature taken by the university board, the vice-chancellor and faculty/department boards.

LiU’s rule book for education at first-cycle and second-cycle levels is available at http://styrdokument.liu.se/Regelsamling/Innehall/Utbildning_pa_grund-_och_avancerad_niva. 

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