Data mining

Website:website containing additional information
Course code:INFOMDM
Credits:7.5 ECTS
Period:periode 1 (week 36 t/m 45, dwz 3-9-2012 t/m 9-11-2012; herkansing week 1)
Timeslot:B
Participants:up till now 56 subscriptions
Schedule:Note: from now on the schedule is to be found in Osiris
Teachers:Dit is een oud rooster!
formgrouptimeweekroomteacher
college   di 9.00-10.4536-44 BBL-083 Ad Feelders
  
do 15.15-17.0037-44 MIN-208
practicum groep 1 di 11.00-12.4537-44 BBL-106 CLZ Ad Feelders
  
groep 2 di 11.00-12.4537-44 BBL-103 CLZ Ad Feelders
  
Contents:The following subjects are discussed:
  • Classification Tree Algorithms
  • Graphical Models (including Bayesian Networks)
  • Frequent Pattern Mining
  • Subgroup Discovery
  • Clustering
  • Ranking
  • Text 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:
Description:Note: This course used to be called Advanced Data Mining, to distinguish it from the bachelor course. Needless to say (but we do it anyway) you can only use one of the two master courses to meet the requirements of your master program.

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.
  • For a given query to a search engine, rank a collection of documents/web pages with respect to their relevance to the query.
  • Analysis of social network, for example, discovering communities on the web.
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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