|
|
| |
| SYLLABUS |
| Data Mining - Clustering and Association Analysis,
15 ECTS Credits |
| |
COURSE CATEGORY Master´s Programme in Statistics, Data Analysis and Knowledge Discovery
MAIN FIELD OF STUDY Datalogi
SUBJECT AREA
|
|
COURSE CODE |
|
732A31 |
|
| AIM OF THE COURSE |
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. This course focus on clustering and association.
Having completed the course, the student should be able to:
- understand and 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
- design and perform a data analysis task using clustering and association analysis for a given data mining problem and evaluate the results |
| CONTENTS |
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
(e.g. K-means), hierarchical clustering methods, density-based clustering
methods (e.g. DBSCAN), cluster evaluation, outlier analysis |
| TEACHING |
| The teaching comprises lectures, seminars, and computer laboratory and project work. Lectures are devoted to theory, concepts and techniques. The techniques are practised in the computer laboratory and project work. The seminars comprise student presentations and discussions of assignments. In addition to those, the student are expected to study on their own. Language of instruction: English. |
| EXAMINATION |
- written examination
- laboratory and project work
Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination.
Students who have passed an examination may not retake it in order to improve their grades. |
| ADMISSION REQUIREMENTS |
Basic programming, statistics, calculus and linear algebra.
Documented knowledge of English equivalent to "Engelska B"; i.e. English as native language or an internationally recognized test, e.g. TOEFL (minimum scores: Paperbased 575 + TWE-score 4.5, internetbased 90 TWE-score 20), IELTS, academic (minimum score: Overall band 6.5 and no band under 5.5), or equivalent. |
| GRADING |
| The course is graded according to the ECTS grading scale A-F |
| CERTIFICATE |
| Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division. |
| COURSE LITERATURE |
| The course literature is decided upon by the department
in question. |
| OTHER INFORMATION |
Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus.
The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed. |
| |
Data Mining - Clustering and Association Analysis
Data Mining - Clustering and Association Analysis |
| |
Department responsible
for
the course or
equivalent:
IDA - Department of Computer and Information |
| |
|
|
|
|
|
| Registrar No: 1330/06-41 |
|
Course Code: 732A31 |
|
|
|
| |
|
Exam codes: see Local Computer System |
|
|
|
| Subject/Subject Area : Datalogi |
|
|
|
|
|
| |
|
|
|
|
|
| Level |
|
Education level |
|
|
Subject Area Code |
|
Field of Education |
|
| A1X |
|
Advanced level |
|
|
|
|
TE |
|
|
The syllabus was approved by the Board of Faculty of Arts and Science 2008-09-10
|
|
|
| |
