Set Matching Measures for External Cluster Validity
Comparing two clustering results of a data set is a challenging task in cluster analysis. Many external validity
measures have been proposed in the literature. A good measure should be invariant to the changes of data size, cluster size
and number of clusters. We give an overview of existing set matching indexes and analyze their properties. Set matching
measures are based on matching clusters from two clusterings. We analyze the measures in three parts: 1. cluster similarity 2.
matching 3. overall measurement.
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This product was added to our catalog on Wednesday 07 June, 2017.