Clustering Software reviews

Cluster analysis has been a data mining tool for some time. There are hundreds of cluster algorithms that compete for various statistical notions of performance. All major statistical software packages offer several solutions to address this task. Recently the notion of Latent Class Analysis has its version in the cluster analysis problem setting, where the unknown number of classes or groups is treated in either a stochastic or deterministic manner. In The American Statistician Feb 2009, Vol. 63 article, Review of Three Latent Class Cluster Analysis Packages: Latent Gold, poLCA, and MCLUST, one finds yet another discussion and comparison of the ever expanding software choices. The point of this note is the solution offered by the MCLUST program is available as free R software of the highest quality and performance. MCLUST performs model based clustering with multivariate normal mixtures. A Bayesian treatment of the latent class problem by MCLUST treats the unknown number of classes/groups as a random variable and its marginal posterior distribution of the number of classes is an outcome!

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