Material Detail

Comparing clustering with confidence

Comparing clustering with confidence

This video was recorded at Workshop on Stability and Resampling Methods for Clustering, Tübingen 2007. Model assessment is one of the most crucial aspects of statistical data analysis problems. In particular in data clustering it is difficult to devise reasonable tools for this purpose - the most prominent example is the problem of choosing the number k of clusters one wants to construct. Stability-based methods and resampling methods have become a popular choice for model selection in clustering, which is documented by the wealth of literature on this topic. The basic rationale of those approaches is that valid models should be reproducible under perturbation or resampling of the data. If high instability of models is observed, the inferred solution does not seem to be a generally valid... Show More
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden