Database Reference
In-Depth Information
Mixture models
A
mixture model
is essentially an extension of the idea behind fuzzy K-means; however, it
makes an assumption that there is an underlying probability distribution that generates the
data. For example, we might assume that the data points are drawn from a set of K-inde-
pendent Gaussian (normal) probability distributions. The cluster assignments are also soft,
so each point is represented by K membership weights in each of the K underlying probab-
ility distributions.
Note
See
http://en.wikipedia.org/wiki/Mixture_model
for further details and for a mathematical
treatment of mixture models.
