Modeling and Multivariate Methods - SAS

Chapter 18 Clustering Data 475
Normal Mixtures
The Cluster Comparison report gives fit statistics to compare different numbers of clusters. For KMeans
Clustering and Self Organizing Maps, the fit statistic is CCC (Cubic Clustering Criterion). For Normal
Mixtures, the fit statistic is BIC or AICc. Robust Normal Mixtures does not provide a fit statistic.
Robust Normal Mixtures
The Robust Normal Mixtures option is available if you suspect you may have outliers in the multivariate
sense. Since regular Normal Mixtures is sensitive to outliers, the Robust Normal Mixtures option uses a
more robust method for estimating the parameters. For details, see “Additional Details for Robust Normal
Mixtures” on page 476.
To perform Robust Normal Mixtures, select that option on the Method menu of the Iterative Clustering
Control Panel (Figure 18.5). After selecting Robust Normal Mixtures, the control panel looks like
Figure 18.9.
Figure 18.9 Normal Mixtures Control Panel
Some of the options on the panel are described in “K-Means Control Panel” on page 470. The other options
are described below:
Diagonal Variance is used to constrain the off-diagonal elements of the covariance matrix to zero. In this
case, the platform fits multivariate normal distributions that have no correlations between the variables.
This is sometimes necessary in order to avoid getting a singular covariance matrix, when there are fewer
observations than columns.
Huber Coverage is a number between 0 and 1. Robust Normal Mixtures protects against outliers by
down-weighting them. Huber Coverage can be loosely thought of as the proportion of the data that is
not considered outliers, and not down-weighted. Values closer to 1 result in a larger proportion of the
data not being down-weighted. In other words, values closer to 1 protect only against the most extreme
outliers. Values closer to 0 result in a smaller proportion of the data not being down-weighted, and may
falsely consider less extreme data points to be outliers.