External validation measures for nested clustering of text documents

Studies in Computational Intelligence

Volumn 369, Issue , 2011, Pages 207-225

  Draszawka, Karol ^a   Szymański, Julian ^a  

^a GDANSK UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Clustering validation; F measure; Hubert's index; OPTICS; Rand index; Reachability plot

Indexed keywords

EID: 80054750771     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-22732-5_18     Document Type: Article

References (24)

1. Manning, C.D., Raghavan, P., Schütze, H.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
2. Gordan, D.A.: A review of hierarchical classification, pp. 119-137 (1987)
3. Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suh, E., Dougherty, E.R.: Model-based evaluation of clustering validation measures. Pattern Recognition 40, 807-824 (2007) (Pubitemid 44667757)
4. Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems 17, 107-145 (2001) (Pubitemid 34050790)
5. Halkidi, M., Vazirgiannis, M.: A density-based cluster validity approach using multi-representatives. Pattern Recognition Letters 29, 773-786 (2008)
6. Milligan, G., Cooper, M.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159-179 (1985)
7. Dimitriadou, E., Dolniar, S., Weingessel, A.: An examination of indexes for determining the number of clusters in binary data sets. Psychometrika 67, 137-159 (2002), doi:10.1007/BF02294713
8. Menardi, G.: Density-based silhouette diagnostics for clustering methods. Statistics and Computing, 1-14 (2010), doi:10.1007/s11222-010-9169-0
9. Zalik, K.R., Zalik, B.: Validity index for clusters of different sizes and densities. Pattern Recognition Letters 32, 221-234 (2011)
10. Aliguliyev, R.M.: Performance evaluation of density-based clustering methods. Information Sciences 179, 3583-3602 (2009)
11. Rand, W.M.: Objective criteria for the evaluation of clustering method. Journal of the American Statistical Association 66, 846-850 (1971)
12. Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2, 193-218 (1985)
13. Milligan, G.W., Soon, S.C., Sokol, L.M.: The effect of cluster size, dimensionality, and the number of clusters on recovery of true cluster structure. IEEE Transactions on Pattern Analysis and Machine Intelligence (1983)
14. Kiritchenko, S., Matwin, S., Nock, R., Famili, F.: Learning and evaluation in the presence of class hierarchies: Application to text categorization. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 395-406. Springer, Heidelberg (2006) (Pubitemid 44077483)
15. James, R.F.: Methods of comparing classifications. Annual Review of Ecology and Systematics 5, 101-113 (1974)
16. Raghavan, V.V., Ip, M.Y.L.: Techniques for measuring the stability of clustering: a comparative study. In: Salton, G., Schneider, H.-J. (eds.) SIGIR 1982. LNCS, vol. 146, pp. 209-237. Springer, Heidelberg (1983) (Pubitemid 15431081)
17. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18, 613-620 (1975)
18. Shalom, M.W., Indhurkya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann, San Francisco (1997)
19. Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002)
20. Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. SIGMOD Rec. 28, 49-60 (1999) (Pubitemid 129597323)
21. Brecheisen, S., Kriegel, H.P., Krger, P., Pfeifle, M.: Visually mining through cluster hierarchies. In: Proceedings of the Fourth SIAM International Conference on Data Mining (2004)
22. Sander, J., Qin, X., Lu, Z., Niu, N., Kovarsky, A.: Automatic extraction of clusters from hierarchical clustering representations. In: Whang, K.Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS, vol. 2637, pp. 567-567. Springer, Heidelberg (2003)
23. Roy, S., Deepak, P.: Optics on text data: Experiments and test results. In: Proceedings of the 2006 SIAM International Conference on Data Mining (2006)
24. Christaine, F. (ed.): WordNet. An Electronic Lexical Database. The MIT Press, Cambridge (1998)