Paper: V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure

ACL ID D07-1043
Title V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

We present V-measure, an external entropy- based cluster evaluation measure. V- measure provides an elegant solution to many problems that affect previously de- fined cluster evaluation measures includ- ing 1) dependence on clustering algorithm or data set, 2) the “problem of matching”, where the clustering of only a portion of data points are evaluated and 3) accurate evalu- ation and combination of two desirable as- pects of clustering, homogeneity and com- pleteness. We compare V-measure to a num- ber of popular cluster evaluation measures and demonstrate that it satisfies several de- sirable properties of clustering solutions, us- ing simulated clustering results. Finally, we use V-measure to evaluate two clustering tasks: document clustering and pitch accent type clustering.