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2019/2020  KAN-CCMVI2088U  Data Mining for Business Intelligence

English Title
Data Mining for Business Intelligence

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Course instructor: Professor Selwyn Piramuthu, Information Systems and Operations Management, University of Florida.
    Sven Bislev - Department of Management, Society and Communication (MSC)
For academic questions related to the course, please contact instructor Selwyn Piramuthu at Selwyn@ufl.edu
Other academic questions: contact academic director Sven Bislev at sb.msc@cbs.dk
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 12/11/2019

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors
  • acquire an overview of data mining methods and their applications and be aware of the similarities and differences among these methods
  • use data mining methods with an awareness of the different characteristics of data
  • select appropriate preprocessing methods to improve data mining performance
  • use data mining with a business intelligence perspective
Course prerequisites
Good knowledge of algebra. No computer programming experience necessary.
Data Mining for Business Intelligence:
Exam ECTS 7.5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer, Ordinary exam: 4 hour written exam in the period of 27–31 July 2020
Retake exam: 4 hour written exams in the period of 28 September–2 October 2020
3rd attempt (2nd retake) exam: 72-hour home assignment- 23-26 November 2020 – for all ISUP courses simultaneously

Exam schedules available on https:/​/​www.cbs.dk/​uddannelse/​international-summer-university-programme-isup/​courses-and-exams
Aids Closed book: no aids
However, at all written sit-in exams the student has access to the basic IT application package (Microsoft Office (minus Excel), digital pen and paper, 7-zip file manager, Adobe Acrobat, Texlive, VLC player, Windows Media Player), and the student is allowed to bring simple writing and drawing utensils (non-digital). PLEASE NOTE: Students are not allowed to communicate with others during the exam.
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Retake exam: 4 hour written sit-in exam, new exam question
Exam form for 3rd attempt (2nd retake): 72-hour home project assignment, max. 10 pages.
Course content, structure and pedagogical approach
The past couple of decades have witnessed an unprecedented explosion in the amount of data collected by businesses. Commercial enterprises have been quick to recognize the strategic significance of extracting valuable information from these large quantities of data, leading to the emergence of the field of Data Mining. Data Mining refers to a family of techniques that are used to detect “interesting" and “useful" nuggets of relationships/knowledge in data. These techniques form the core of most currently used intelligent decision support systems that involve huge amounts of data. This course will provide an overview of some commonly used Data Mining methods and relate them to appropriate Business Intelligence issues. We will use appropriate Data Mining tools, when available, throughout the course. This course does not involve any computer programming exercise and no previous programming experience is necessary to successfully complete this course.
The main topics covered include:
• Introduction and general characteristics of data and data mining
• Data warehousing
• Data transformation/cleaning
• Data preprocessing
• Recursive partitioning
• Association rules
• Neural networks
• Genetic algorithms
• Clustering
• Fuzzy logic
• Web mining
Preliminary assignment:
Class 1: Introduction and course overview; Introduction to Weka
Class 2: Recursive partitioning
Class 3: Association rules
Class 4: Uncertainty in knowledge-based systems
Class 5: Data transformation/cleaning
Homework midterm assessment (review)
Class 6: Neural networks
Feedback activity:
Class 7: Genetic algorithms
Class 8: Expert systems
Class 9: Fuzzy logic
Class 10: Web mining
Class 11: Applications
Description of the teaching methods
All teaching takes place on campus (notice that face-to-face teaching may include the use of online materials and tools.
Feedback during the teaching period
A midterm assessment will be administered. The format will be similar to that of the 4-hour Ordinary Exam administered at the end of the course. Questions will be based on the concepts that are covered during the first five class meetings.
Student workload
Preliminary assignment 20 hours
Classroom attendance 33 hours
Preparation 126 hours
Feedback activity 7 hours
Examination 20 hours
Further Information
Preliminary Assignment: To help students get maximum value from ISUP courses, instructors provide a reading or a small number of readings or video clips to be read or viewed before the start of classes with a related task scheduled for class 1 in order to 'jump-start' the learning process.
Course timetable is available on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams
We reserve the right to cancel the course if we do not get enough applications. This will be communicated on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams end March 2020.
Expected literature

Mandatory readings: 


We will use the following research publications, some of which started a sub-area in machine learning, to guide class lectures. The students will have access to these publications at the beginning of the course.
Adams. A probability model of medical reasoning and the MYCIN model. Mathematical Biosciences. 32(1-2), 177-186, 1976.
Agrawal, Srikant. Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference, 487-499, 1994.
Bebis, Georgiopoulos. Feed-forward Neural Networks: Why Network Size is so Important. IEEE Potentials, October/November, 27-31, 1994.
Duda, Shortliffe, Expert Systems Research. Science, 220(4594), 261-268, 15 April 1983.
Kosala, Blockeel. Web Mining Research: A Survey. SIGKDD Explorations. 2(1), 1-15, December 2001.
Piramuthu. On preprocessing data for financial credit risk evaluation. Expert Systems with Applications. 30, 489 -497, 2006.

Quinlan. Induction of Decision Trees. Machine Learning 1, 81-106, 1986.
Whitley. A Genetic Algorithm Tutorial. Statistics and Computing. 4, 65-85, 1994.
Zadeh. Fuzzy Sets. Information and Control. 8, 338-353, 1965.
Last updated on 12/11/2019