document: Assessment and pruning of hierarchical model based clustering The goal of clustering is to identify distinct groups in a dataset . The basic idea of model-based clustering is to approximate the data density by a mixture model , typically a mixture of Gaussians , and to estimate the parameters of the component densities , the mixing fractions , and the number of components from the data . The number of distinct groups in the data is then taken to be the number of mixture components , and the observations are partitioned into clusters ( estimates of the groups ) using Bayes ' rule . If the groups are well separated and look Gaussian , then the resulting clusters will indeed tend to be `` distinct '' in the most common sense of the word - contiguous , densely populated areas of feature space , separated by contiguous , relatively empty regions . If the groups are not Gaussian , however , this correspondence may break down ; an isolated group with a non-elliptical distribution , for example , may be modeled by not one , but several mixture components , and the corresponding clusters will no longer be well separated . We present methods for assessing the degree of separation between the components of a mixture model and between the corresponding clusters . We also propose a new clustering method that can be regarded as a hybrid between model-based and nonparametric clustering . The hybrid clustering algorithm prunes the cluster tree generated by hierarchical model-based clustering . Starting with the tree corresponding to the mixture model chosen by the Bayesian Information Criterion , it progressively merges clusters that do not appear to correspond to different modes of the data density .
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