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# ELE 3909 Cluster Analysis for Business

## ELE 3909 Cluster Analysis for Business

Course code:
ELE 3909
Department:
Data Science and Analytics
Credits:
7.5
Course coordinator:
Course name in Norwegian:
Product category:
Bachelor
Portfolio:
Bachelor - Programme Electives
Semester:
2022 Autumn
Active status:
Active
Level of study:
Bachelor
Teaching language:
English
Course type:
One semester
Introduction

Clustering methods help to design segment-based strategies in business, e.g., design marketing campaigns, improve the classification of customer preferences, or to assess credit risk more accurate just to name a few examples. These methods are also useful to reveal subgroups, or  in a larger population that share similar characteristics.

This course introduces different clustering techniques to find smaller groups within a larger population as well as methods to analyse and understand which characteristics distinguish each cluster. Further, the course introduces the idea of dynamic clustering, or time dependent clustering, where we learn how to follow movements between clusters through time.

The focus of the course is practical and analytical. preparing the students for jobs as data analysts/scientists or for a MSc in Data Science for Business or in Business Analytics.

Learning outcomes - Knowledge

In this course the students will:

• Learn the difference between supervised and unsupervised learning
• Learn how to prepare and/or transform a data set for cluster analysis
• Get a thorough introduction to different clustering algorithms
• Learn how to select the optimal number of clusters
• Learn to analyze the clustering results
• Understand the differences between clustering and dynamic clustering
Learning outcomes - Skills

After the completion of this course, students will be able to:

• Prepare data sets, including data representations, for cluster analysis
• Apply different cluster techniques for a given task
• Choose the number of clusters using an appropriate metric
• Use statistical methods to analyse the results
General Competence

Students should be able to understand the principles of unsupervised learning and how clustering methods can be applied in different applications. A successful student will be able to select a clustering algorithm, carry out an analysis that reveal hidden patterns in the data, or a representation of it, and bring added-value into the application of the analysis.

Course content
• Unsupervised vs Supervised learning
• Key clustering algorithms, including: Hierarchical clustering, K-means, and density-based clustering methods
• Cluster analysis on different feature spaces
• Statistical methods to analyze the clusters
• Clustering vs Dynamic clustering
Teaching and learning activities

Lectures and practical exercises that must be solved with a computer using Python. The practical exercises are shared via GitHub.

Software tools
Software defined under the section "Teaching and learning activities".

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

Qualifications

Higher Education Entrance Qualification

### Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

EBA 3400 Programming, data extraction and visualisation, EBA 1180 Mathematics for Data Science, and EBA 2904 Statistics with programming - or equivalent.

Students who do not have EBA 1180 Mathematics for Data Science can compensate with MET 2910 Mathematics or EBA 2910 Mathematics plus ELE 3776 Mathematical Analysis (or ELE 3719 Mathematics elective).

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
ELE 39091
ECTS
Two examiners
Resit:
Examination when next scheduled course
40No1 Week(s)Group (2 - 3)Written home exam. Work in groups of up to three. Requires Python. All exams must be passed to obtain a final grade in the course.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
ELE 39092
ECTS
Two examiners
Resit:
Examination when next scheduled course
60Yes3 Hour(s)
• BI-approved exam calculator
• Simple calculator
• Bilingual dictionary
Individual Written school exam. All exams must be passed to obtain final grade in the course.
Exams:
 Exam category: Submission Form of assessment: Written submission Weight: 40 Invigilation: No Grouping (size): Group (2-3) Support materials: Duration: 1 Week(s) Comment: Written home exam. Work in groups of up to three. Requires Python. All exams must be passed to obtain a final grade in the course. Exam code: ELE 39091 Grading scale: ECTS Resit: Examination when next scheduled course
 Exam category: Submission Form of assessment: Written submission Weight: 60 Invigilation: Yes Grouping (size): Individual Support materials: BI-approved exam calculator Simple calculator Bilingual dictionary Duration: 3 Hour(s) Comment: Written school exam. All exams must be passed to obtain final grade in the course. Exam code: ELE 39092 Grading scale: ECTS Resit: Examination when next scheduled course
Type of Assessment:
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight:
100