Clustering is a multivariate technique that groups together observations that share similar values across a number of variables. Typically, observations are not scattered evenly through ndimensional space, but rather they form clumps, or clusters. Identifying these clusters provides you with a deeper understanding of your data.
Note: JMP also provides a platform that enables you to cluster variables. See the Cluster Variables topic.
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K Means Cluster is appropriate for larger tables with up to millions of rows and allows only numerical data. You need to specify the number of clusters, k, in advance. The algorithm guesses at cluster seed points. It then conducts an iterative process of alternately assigning points to clusters and recalculating cluster centers.

Some of the clustering platforms have options to handle outliers in the data. However, if your data has outliers, it is best to explore them first prior to analyzing. This can be done using the Explore Outliers Utility. For more information, see Explore Outliers Utility in the Predictive and Specialized Modeling book.