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http://dx.doi.org/10.11627/jkise.2012.35.4.244

$F_n$-Measure : An External Cluster Evaluation Measure  

Kim, Kyeongtaek (Department of Industrial and Management Engineering, Hannam University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.35, no.4, 2012 , pp. 244-248 More about this Journal
Abstract
F-Measure is one of the external measures for evaluating the validity of clustering results. Though it has clear advantages over other widely used external measures such as Purity and Entropy, F-Measure has inherently been less sensitive than other validity measures. This insensitivity owes to the definition of F-Measure that counts only most influential portions. In this research, we present $F_n$-Measure, an external cluster evaluation measure based on F-Measure. $F_n$-Measure is so sensitive that it can detect their difference in the cases that F-Measure cannot detect the difference in clustering results. We compare $F_n$-Measure to F-Measure for a few clustering results and show which measure draws better result based upon homogeneity and completeness.
Keywords
External Clustering Measure; F-Measure; $F_n$-Measure;
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1 Xu, L., Mo, H., and Wang, K., Immune Algorithm for Supervised Clustering. Proceedings of the 5th IEEE International Conference on Cognitive Informatics, 2006, p 953-958.
2 Xu, R. and Wunsch, D. II, Survey of Clustering Algorithms. IEEE Transactions on neural Networks, 2005, Vol. 16, No. 3, p 645-678.   DOI   ScienceOn
3 Halkidi, M., Batistakis, Y., and Vazirgiannis, M., Cluster Validity Methods : Part I, SIGMOD Record, 2002, Vol. 31, No. 2, p 40-45.   DOI   ScienceOn
4 Zhao, Y. and Karypis, G., Criterion functions for document clustering : Experiments and Analysis. Technical Report TR 01-40, Dept. of Computer Science, U. of Minnesota, 2001.
5 Rosenberg, A. and Hirschberg, J., V-Measure : A Conditional Entropy-based External Cluster Evaluation Measure, Proceedings of the 2007 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 2007, p 410-420.
6 Melia, M., Comparing Clustering-an Information Based Distance. J. of Multivariate Analysis, 2007, Vol. 98, p 873-895.   DOI   ScienceOn
7 Berry, M. and Linoff, G., Data Mining Techniques for Marketing, Sales and Customer Support, John Wiley and Sons, 1996.
8 Reichart, R. and Rappoport, A., The NVI Clustering Evaluation Measure. Proceedings of the Thirteenth Conference on Computational Natural Language Learning, 2009, p 165-173.
9 Amigo, E., Gonzalo, J., and Artiles, J., A Comparison of Extrinsic Clustering Evaluation Metrics based on Formal Constraints. Information Retrieval, Aug 2009, Vol. 12, No. 4, p 461-486.   DOI   ScienceOn
10 Wu, J., Chen, J., Xiong, H., and Xie, M., External Validation measures for K-means Clustering : A Data Distribution perspective. Expert Systems with Applications, 2009, Vol. 36, p 6050-6061.   DOI   ScienceOn
11 Larson, B. and Aone, C., Fast and Effective Text Mining Using Linear Time Document Clustering. Proceedings of the Conference on Knowledge Discovery and Data Mining, 1999, p 16-22.