Normal Mixtures

Use Normal Mixtures for clustering when your data come from overlapping normal distributions. You need to specify the number of clusters in advance.

Normal mixtures is an iterative technique based on the assumption that the joint probability distribution of the observations is approximated using a mixture of multivariate normal distributions. These mixtures represent different clusters. The individual clusters have multivariate normal distributions.

When clusters are well separated, hierarchical and k-means clustering work well. But when clusters overlap, normal mixtures provides a better alternative, because it is based on cluster membership probabilities, rather than arbitrary cluster assignments based on borders.

Figure 13.1 Normal Mixtures Biplot

Contents

Overview of the Normal Mixtures Clustering Platform

Overview of Platforms for Clustering Observations

Example of Normal Mixtures Clustering

Launch the Normal Mixtures Clustering Platform

Model Based Clustering Report

Model Based Clustering Options

Model Based Clustering Control Panel

Normal Mixtures NCluster=<k> Report

Cluster Comparison Report

Normal Mixtures NCluster=<k> Report

Normal Mixtures NCluster=<k> Report Options

Statistical Details for the Normal Mixtures Clustering Platform