Clustering models uncover natural groupings (clusters) in the data. Members of the same cluster are more like ("closer to") each other than they are like members of a different cluster.
Clustering can be a useful data-preprocessing step to identify homogeneous groups on which to build predictive models.
In ODM a cluster is characterized by its centroid, attribute histograms, and place in the clustering model hierarchical tree. ODM performs hierarchical clustering using one of the following algorithms:
The clusters discovered by these algorithms are used to create rules that capture the main characteristics of the data assigned to each cluster.
The clusters are also used to generate a Bayesian probability model that is used during scoring for assigning data points to clusters.
After you build a clustering model, you can apply it to new data. For a brief overview of the apply process, see Apply a Model.
For more information about clustering, see Where to Find More Information.
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