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.

Advantages and Disadvantages of Mixture Models

  • Strength
    • Mixture models are more general than partitioning and fuzzy clustering
    • Clusters can be characterized by a small number of parameters
    • The results may satisfy the statistical assumptions of the generative models
  • Weakness
    • Converge to local optimal (overcome: run multi-times w. random initialization)
    • Computationally expensive if the number of distributions is large, or the data set contains very few observed data points
    • Need large data sets
    • Hard to estimate the number of clusters

Speaker notes:

Content Tools


There are currently no sources for this slide.