The Orthogonal Partitioning Cluster (O-Cluster) algorithm is used to solve clustering problems. O-Cluster creates a hierarchical grid-based clustering model, that is, it creates axis-parallel (orthogonal) partitions in the input attribute space. The algorithm operates recursively. The resulting hierarchical structure represents an irregular grid that tessellates the attribute space into clusters. The resulting clusters define dense areas in the attribute space. The clusters are described by intervals along the attribute axes and the corresponding centroids and histograms. A parameter called sensitivity defines a baseline density level. Only areas with peak density above this baseline level can be identified as clusters.
Note that the algorithm determines the number of clusters; the user can specify a maximum number of clusters.
The clusters discovered by O-Cluster are used to generate a Bayesian probability model that is then used during model apply (scoring) for assigning data points to clusters.
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