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R Data Analysis Cookbook - Second Edition by Kuntal Ganguly

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How it works...

Model-based clustering tries to recover the distribution from the data assuming that the data is generated by an underlying probability distribution. One common model-based technique is using finite mixture models, which provide a modeling framework for the analysis of the probability distribution.

Model-based clustering process has several steps. First, the process selects the number and types of probability distribution component. Next, it fits a finite mixture model to calculate the posterior probabilities of a component membership. Lastly, it assigns the membership of each observation to the component with the maximum probability.

In this recipe, we first install and load the Mclust library and then fit the multishapes ...

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