Multivariate Methods • K Means Cluster • Launch the K Means Cluster Platform
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Launch the K Means Cluster Platform
Launch the K Means Cluster platform by selecting Analyze > Clustering > K Means Cluster. K Means Cluster Launch Window shows the Clustering launch window for Cytometry.jmp.
K Means Cluster Launch Window
Y, Columns
The variables used for clustering observations.
Note: K-Means clustering supports only numeric columns.
Weight
A column whose numeric values assign a weight to each row in the analysis.
Freq
A column whose numeric values assign a frequency to each row in the analysis.
By
A column whose levels define separate analyses. For each level of the specified column, the corresponding rows are analyzed. The results are presented in separate reports. If more than one By variable is assigned, a separate analysis is produced for each possible combination of the levels of the By variables.
Launch Window Options
KMeans is the default clustering method, but you have the option to select Hierarchical or Normal Mixtures. If you select KMeans or Normal Mixtures, when you click OK, a Control Panel appears. See Iterative Clustering Control Panel.
Columns Scaled Individually
Scales each column independently of the other columns. Use when variables do not share a common measurement scale, and you do not want one variable to dominate the clustering process. For example, one variable might have values that are between 0 and 1000, and another variable might have values between 0 and 10. In this situation, you can use the option so that the clustering process is not dominated by the first variable.
Johnson Transform
Fits a Johnson family distribution to the Y variables. If Columns Scaled Individually is selected, a separate Johnson transformation is fit to each Y variable. If Columns Scaled Individually is not selected, a single Johnson transformation is fit to values for all the Y variables. Two of the Johnson families (Sb and Su) are considered and the fitting method uses maximum likelihood. See “Distributions” in the Basic Analysis book.
The Johnson transformations attempt to normalize the data, mitigating skewness, and pulling outliers in toward the center of the distribution.

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Help created on 9/19/2017