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primarykey
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2052857
data
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2010-01-12T22:14:13.457
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2010-01-12T22:19:58.047
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2010-01-12T22:19:58.047
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2052321
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2
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text
Body
There are many choices, but if you are interested in the probability that a new data point belongs to a particular mixture, I would use a probabilistic approach such as Gaussian mixture modeling either estimated by maximum likelihood or Bayes. Maximum likelihood estimation of <a href="http://www.mathworks.com/access/helpdesk/help/toolbox/stats/bq_679x-24.html" rel="noreferrer">mixtures models is implemented in Matlab</a>. Your requirement that the number of components is unknown makes your model more complex. The dominant probabilistic approach is to place a Dirichlet Process prior on the mixture distribution and estimate by some Bayesian method. For instance, see <a href="http://www.gatsby.ucl.ac.uk/~edward/pub/inf.mix.nips.99.pdf" rel="noreferrer">this paper on infinite Gaussian mixture models</a>. The DP mixture model will give you inference over the number of components and the components each elements belong to, which is exactly what you want. Alternatively you could perform model selection on the number of components, but this is generally less elegant. There are many implementation of DP mixture models models, but they may not be as convenient. For instance, here's a <a href="http://people.csail.mit.edu/jacobe/software.html" rel="noreferrer">Matlab implementation</a>. Your graph suggests you are an R user. In that case, if you are looking for prepacked solutions, the answer to your question lies on this <a href="http://cran.r-project.org/web/views/Cluster.html" rel="noreferrer">Task View for cluster analysis</a>.
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