Machine Learning with R Cookbook - Second Edition

How it works...

Instead of taking a heuristic approach to building a cluster, model-based clustering uses a probability-based approach. Model-based clustering assumes that the data is generated by an underlying probability distribution and tries to recover the distribution from the data. One common model-based approach is using finite mixture models, which provide a flexible modeling framework for the analysis of the probability distribution. Finite mixture models are a linearly weighted sum of component probability distribution. Assume the data y=(y₁,y₂...y_n) contains n independent and multivariable observations; G is the number of components; the likelihood of finite mixture models can be formulated as:

Where and are the density and parameters ...