Current Slide

Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.

Probabilistic Hierarchical Clustering

  • Algorithmic hierarchical clustering
    • Nontrivial to choose a good distance measure
    • Hard to handle missing attribute values
    • Optimization goal not clear: heuristic, local search
  • Probabilistic hierarchical clustering
    • Use probabilistic models to measure distances between clusters
    • Generative model: Regard the set of data objects to be clustered as a sample of the underlying data generation mechanism to be analyzed
    • Easy to understand, same efficiency as algorithmic agglomerative clustering method, can handle partially observed data
  • In practice, assume the generative models adopt common distributions functions, e.g., Gaussian distribution or Bernoulli distribution, governed by parameters


Speaker notes:

Content Tools

Sources

There are currently no sources for this slide.