We present V-measure, an external entropybased cluster evaluation measure. V-measure provides an elegant solution to many problems that affect previously defined cluster evaluation measures including 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 evaluation and combination of two desirable aspects of clustering, homogeneity and completeness. We compare V-measure to a number of popular cluster evaluation measures and demonstrate that it satisfies several desirable properties of clustering solutions, using simulated clustering results. Finally, we use V-measure to evaluate two clustering tasks: document clustering and pitch accent type clustering. © 2007 Association for Computational Linguistics.
CITATION STYLE
Rosenberg, A., & Hirschberg, J. (2007). V-Measure: A conditional entropy-based external cluster evaluation measure. In EMNLP-CoNLL 2007 - Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 410–420).
Mendeley helps you to discover research relevant for your work.