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Data Mining - Clustering and Association Analysis, 6 credits
Data Mining - Clustering and Association Analysis, 6 hp
TDDD41
Main field of study
Information Technology Computer Science and Engineering Computer ScienceCourse level
Second cycleCourse type
Programme courseExaminer
Patrick LambrixDirector of studies or equivalent
Patrick LambrixEducation components
Preliminary scheduled hours: 26 hRecommended self-study hours: 134 h
Available for exchange students
YesECV = Elective / Compulsory / Voluntary
Main field of study
Information Technology, Computer Science and Engineering, Computer ScienceCourse level
Second cycleAdvancement level
A1XCourse 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
| LAB1 | Laboratory work | 2 credits | U, G |
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
Grades
Four-grade scale, LiU, U, 3, 4, 5Department
Institutionen för datavetenskapDirector of Studies or equivalent
Patrick LambrixExaminer
Patrick LambrixCourse website and other links
http://www.ida.liu.se/~TDDD41Education components
Preliminary scheduled hours: 26 hRecommended self-study hours: 134 h
| Code | Name | Scope | Grading scale |
|---|---|---|---|
| LAB1 | Laboratory work | 2 credits | U, G |
| TEN1 | Written examination | 4 credits | U, 3, 4, 5 |
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.
Course literature is preliminary.
There is no course literature available for this course in studieinfo.
Note: The course matrix might contain more information in Swedish.
I = Introduce, U = Teach, A = Utilize
| I | U | A | Modules | Comment | ||
|---|---|---|---|---|---|---|
| 1. DISCIPLINARY KNOWLEDGE AND REASONING | ||||||
| 1.1 Knowledge of underlying mathematics and science (G1X level) |
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X
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| 1.2 Fundamental engineering knowledge (G1X level) |
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X
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X
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LAB1
TEN1
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| 1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level) |
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| 1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level) |
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| 1.5 Insight into current research and development work |
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| 2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES | ||||||
| 2.1 Analytical reasoning and problem solving |
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X
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X
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LAB1
TEN1
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| 2.2 Experimentation, investigation, and knowledge discovery |
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X
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X
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LAB1
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| 2.3 System thinking |
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X
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X
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LAB1
TEN1
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| 2.4 Attitudes, thought, and learning |
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X
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X
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LAB1
TEN1
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| 2.5 Ethics, equity, and other responsibilities |
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| 3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION | ||||||
| 3.1 Teamwork |
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X
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LAB1
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| 3.2 Communications |
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X
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LAB1
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| 3.3 Communication in foreign languages |
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X
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| 4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT | ||||||
| 4.1 External, societal, and environmental context |
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| 4.2 Enterprise and business context |
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| 4.3 Conceiving, system engineering and management |
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| 4.4 Designing |
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| 4.5 Implementing |
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| 4.6 Operating |
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| 5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS | ||||||
| 5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development |
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| 5.2 Economic conditions for knowledge development |
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| 5.3 Identification of needs, structuring and planning of research or development projects |
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| 5.4 Execution of research or development projects |
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| 5.5 Presentation and evaluation of research or development projects |
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