Department of Information and Computing Sciences

Departement Informatica Onderwijs
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Onderwijs Informatica en Informatiekunde

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Data mining

Website:website containing additional information
Course code:INFOMDM
Credits:7.5 ECTS
Period:period 1 (week 36 through 45, i.e., 5-9-2016 through 11-11-2016; retake week 1)
Timeslot:D234
Participants:up till now 103 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:Dit is een oud rooster!
formgrouptimeweekroomteacher
lab session group 1 Wed 17.15-19.0036-45 BBG-115 CLZ studentassistent MV
group 2 Wed 17.15-19.0036-45 BBG-109 CLZ studentassistent SL
group 3 Wed 17.15-19.0036-45 BBG-106 CLZ
lecture   Wed 15.15-17.0037 ANDRO-C101 Ad Feelders
38 KBG-ATLAS
39-44 RUPPERT-PAARS
Fri 9.00-10.4536 ANDRO-C101
37-40 KBG-ATLAS
41 RUPPERT-WIT
42-44 KBG-ATLAS
Exam:
week: 45Wed 7-11-201813.30-16.30 uurroom: OLYMPOS-HAL1
week: 1Wed 2-1-201913.30-16.30 uurroom: EDUC-BETAretake exam
Contents:Note: basic knowledge of probability, statistics and calculus is presupposed.
Also, you should be able to write a program, but experience with the R language is not required.

The following subjects are discussed:

  • Classification Tree Algorithms
  • Graphical Models (including Bayesian Networks)
  • Frequent Pattern Mining
  • Subgroup Discovery
  • Social Network Mining
Literature:Lecture notes and selected articles.
Course form:

Lectures and Computer Lab.

Exam form:Written exam and practical assignments.
Minimum effort to qualify for 2nd chance exam:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.
Description:

The amount of data that is produced and stored by companies and other organizations is still growing every day.
In addition, the amount of information available on the web/social media is growing fast. If properly processed and analyzed, this data can be a valuable source of knowledge.
Data mining provides the theory, techniques and tools to extract knowledge from data.

Examples of problems that data mining can help address are:

  • Identify the risk factors for prostate cancer on the basis of clinical and demographic variables.
  • Make a segmentation into groups of similar customers based on their characteristics and purchase bahaviour.
  • Determine which products are frequently bought together in one transaction by customers of a supermarket or web shop.
  • Predict whether two people on a social network site will become friends.
Learning models from data can also be an important part of building a decision support system. In turn, the computer plays an increasingly important role in data analysis:
through the use of computers, computationally expensive data mining methods can be applied that were not even considered in the early days of statistical data analysis.

In this course we study a number of well-known data mining algorithms. We discuss what type of problems they are suited for, their computational complexity and how to interpret
and apply the models constructed with them.

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