Department of Information and Computing Sciences

Departement Informatica Onderwijs
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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., 2-9-2019 through 8-11-2019; retake week 2)
Timeslot:D
Participants:up till now 158 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture   Wed 15.15-17.0037-40 ANDRO-C101 Ad Feelders
42-44 ANDRO-C101
Fri 9.00-10.4536 BOL-1.206
37-44 KBG-COSMOS
tutorial group 1 Wed 17.15-19.0037-40 BBG-214 Steven Langerwerf
42-44 BBG-214
group 2 Wed 17.15-19.0037-40 BBG-209
42-44 BBG-209
Exam:
week: 45Wed 6-11-201913.30-16.30 uurroom: OLYMPOS-HAL2
week: 2Wed 8-1-202017.00-20.00 uurroom: EDUC-GAMMAretake exam
Note:No up-to-date course description available.
Text below is from year 2018/2019
Contents:For questions about enrollment, registration, waiting lists, admittance, etc. please contact the student desk at science.gsns@uu.nl.

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, Bagging and Random Forests
  • Graphical Models (including Bayesian Networks)
  • Frequent Pattern Mining
  • Text Mining
  • Social Network Mining
Literature:May change!
Lecture notes and selected articles/book chapters.
Course form:Lectures and Computer Lab.
Exam form:Written exam and practical assignments.
Minimum effort to qualify for 2nd chance exam:To qualify for the retake exam, the grade of the original must be at least 4.
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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