Hierarchical and k-means clustering methods work well when clusters are well separated, but when clusters overlap, assigning each point to one cluster is problematic. In the overlap areas, there are points from several clusters sharing the same space. It is especially important to use normal mixtures rather than k-means clustering if you want an accurate estimate of the total population in each group, because it is based on membership probabilities, rather than arbitrary cluster assignments based on borders.

To perform Normal Mixtures, select that option on the Method menu of the Iterative Clustering Control Panel (Iterative Clustering Control Panel). After selecting Normal Mixtures, the control panel looks like Normal Mixtures Control Panel.

Some of the options on the panel are described in K-Means Control Panel. The other options are described below:

For an example of Normal Mixtures, open the Iris.jmp sample data table. This data set was first introduced by Fisher (1936), and includes four different measurements: sepal length, sepal width, petal length, and petal width, performed on samples of 50 each for three species of iris.

Note: Your results may not exactly match these results due to the random selection of initial centers.

On the Cluster launch dialog, assign all four variables to the Y, Columns role, select KMeans from Method menu, and click OK. Select Normal Mixtures from the Method menu, specify 3 for the Number of Clusters, and click Go. The report is shown in Normal Mixtures Report.

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.

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 Statistical Details for Robust Estimation Methods.

To perform Robust Normal Mixtures, select that option on the Method menu of the Iterative Clustering Control Panel (Iterative Clustering Control Panel). After selecting Robust Normal Mixtures, the control panel looks like Robust Normal Mixtures Control Panel.

Some of the options on the panel are described in K-Means Control Panel. The other options are described below: