Advanced data mining

Website:website met extra informatie
Onderwijs:Het vak INFOADM wordt in 2012/2013 niet aangeboden.
Onderwijs:Het is nog niet zeker of en zo ja in welke periode van 2012/2013 dit vak wordt aangeboden.
Nota bene:Er is geen recente vakbeschrijving beschikbaar.
Onderstaande tekst is een oude vakbeschrijving uit collegejaar 2011/2012
Inhoud:Topics
  • The Knowledge Discovery Process
  • Classification Tree Algorithms
  • Exploratory Data Analysis
  • Graphical Models (including Bayesian Networks)
  • Frequent Pattern Mining
  • Subgroup Discovery
Literatuur:kan veranderen!
Lecture Notes "Advanced Data Mining" and selected papers.
Werkvorm:Lectures and Computer Lab.
Toetsvorm:Written exam and two practical assignments.
Inspanningsverplichting voor aanvullende toets:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.
Beschrijving:

The amount of data that is produced and stored by organisations is still growing almost every day.
This data needs to be processed and analysed to turn it into information and knowledge.
Knowledge thus obtained can improve our understanding and support decision making.
Some problems that data mining can help to solve:

  • For an incoming e-mail message, determine whether it's spam or not.
  • Identify the risk factors for prostate cancer on the basis of clinical and demographic variables.
  • Make a segmentation into groups of similar customers on the basis of their characteristics and purchase bahaviour.
  • Which products are typically bought together in one transaction by customers?
Learning models from data can be an important part of building an intelligent 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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