Normal Mixtures Clustering Platform Overview | JMP 13.2

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Multivariate Methods • Normal Mixtures • Normal Mixtures Clustering Platform Overview

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Normal Mixtures Clustering Platform Overview

Normal Mixtures is one of four platforms that JMP provides for clustering observations. For a comparison of all four methods, see Overview of Platforms for Clustering Observations.

Normal mixtures is an iterative clustering technique for numerical variables. However, it also predicts the proportion of responses expected within each cluster. Normal mixtures assumes that the joint probability distribution of the measurement columns can be approximated using a mixture of multivariate normal distributions, which represent different clusters. Mean vectors and covariance matrices are estimated for each cluster. See McLachlan and Krishnan (1997) and Section 9.6 in Hand et al. (2001).

Note: The Normal Mixtures algorithm involves iterating through random guesses for the cluster centers. Because of this, results from different runs of the analysis might differ slightly.

If you suspect that you have multivariate outliers, you have two options. You can use an outlier cluster or you can select the Robust Normal Mixtures method. The outlier cluster option assumes a uniform distribution and the Robust Normal Mixtures option uses a robust method for estimating the parameters. These methods are less sensitive to outliers than the Normal Mixtures method. For details, see Outlier Cluster and Additional Details for Robust Normal Mixtures.

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Help created on 9/19/2017