Centroid index: Cluster level similarity measure

• Cluster level similarity measure.
• The measure is intuitive, simple to implement and fast to compute.
• Invariant to model mismatch.
• Expected to generalize other clustering models beyond the centroid-based k-means.

In clustering algorithm, one of the main challenges is to solve the global allocation of the clusters instead of just local tuning of the partition borders. Despite this, all external cluster validity indexes calculate only point-level differences of two partitions without any direct information about how similar their cluster-level structures are. In this paper, we introduce a cluster level index called centroid index. The measure is intuitive, simple to implement, fast to compute and applicable in case of model mismatch as well. To a certain extent, we expect it to generalize other clustering models beyond the centroid-based k-means as well.

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