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
Tools
Sources (0)
Tags (0)
Comments (0)
History
Usage
Questions (0)
Playlists (0)
Sources
There are currently no sources for this slide.