Set Matching Measures for External Cluster Validity

 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.