Инд. авторы:	Berikov V., Pestunov I.
Заглавие:	Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties
Библ. ссылка:	Berikov V., Pestunov I. Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties // Pattern Recognition. - 2017. - Vol.63. - P.427-436. - ISSN 0031-3203.
Внешние системы:	DOI: 10.1016/j.patcog.2016.10.017; РИНЦ: 29472082; SCOPUS: 2-s2.0-84998679702; WoS: 000389785900034;
Реферат:	eng: We consider an approach to ensemble clustering based on weighted co-association matrices, where the weights are determined with some evaluation functions. Using a latent variable model of clustering ensemble, it is proved that, under certain assumptions, the clustering quality is improved with an increase in the ensemble size and the expectation of evaluation function. Analytical dependencies between the ensemble size and quality estimates are derived. Theoretical results are supported with numerical examples using Monte-Carlo modeling and segmentation of a real hyperspectral image under presence of noise channels. © 2016 Elsevier Ltd
Ключевые слова:	Clustering Ensemble; Co-association matrix; Ensemble size; Error bound; Latent variable modeling; Cobalt compounds; Hyper-spectral images; Cluster validity index; Spectroscopy; Quality control; Image segmentation; Function evaluation; Cobalt; Weighted clustering ensemble; Latent variable model; Hyperspectral image segmentation; Error bound; Ensemble size; Cluster validity indices; Co-association matrix;
Издано:	2017
Физ. характеристика:	с.427-436
Цитирование:	1. [1] Duda, R.O., Hart, P.E., Stork, D.G., Pattern Classification. Second edition, 2000, Wiley, NY. 2. [2] Jain, A.K., Dubes, R.C., Algorithms for Clustering Data. 1988, Prentice Hall, NJ. 3. [3] Jain, A.K., Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31:8 (2010), 651–666. 4. [4] Ghosh, J., Acharya, A., Cluster ensembles. Wiley Interdiscip. Rev.: Data Min. Knowl. Discov. 1:5 (2011), 305–315. 5. [5] Vega-Pons, S., Ruiz-Shulcloper, J., A survey of clustering ensemble algorithms. IJPRAI 25:3 (2011), 337–372. 6. [6] Breiman, L., Random forests. Mach. Learn. 45:1 (2001), 5–32. 7. [7] Kuncheva, L., Combining Pattern Classifiers. Methods and Algorithms. 2004, Wiley, NJ. 8. [8] Buza, K., Nanopoulos, A., Horvath, T., Schmidt-Thieme, L., GRAMOFON: general model-selection framework based on networks. Neurocomputing 75:1 (2012), 163–170. 9. [9] Fred, A., Jain, A., Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005), 835–850. 10. [10] Frossyniotis, D., Likas, A., Stafylopatis, A., A clustering method based on boosting. Pattern Recognit. Lett. 25:7 (2004), 641–654. 11. [11] A. Topchy, M. Law, A. Jain, A. Fred, Analysis of consensus partition in cluster ensemble, in: Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04), 2004, pp. 225–232. 12. [12] T. Li, C. Ding, Weighted consensus clustering, in: Proceedings of the 2008 SIAM International Conference on Data Mining, SDM, 2008, pp. 798–809. 13. [13] F. Gullo, A. Tagarelli, S. Greco, Diversity-based weighting schemes for clustering ensembles, in: Proceedings of the 2009 SIAM International Conference on Data Mining, SDM, 2009, pp. 437–448. 14. [14] Fern, X.Z., Lin, W., Cluster ensemble selection. J. Stat. Anal. Data Min. 1:3 (2008), 128–141. 15. [15] Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P., Moderate diversity for better cluster ensembles. Inf. Fusion. 7:3 (2006), 264–275. 16. [16] Naldi, M.C., Carvalho, A.C.P.L.F., Campello, R.J.G.B., Cluster ensemble selection based on relative validity indexes. Data Min. Knowl. Discov. 27 (2013), 259–289. 17. [17] Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perez, J., Perona, I., An extensive comparative study of cluster validity indices. Pattern Recognit., 2013, 243–256. 18. [18] S. Vega-Pons, J. Correa-Morris, J. Ruiz-Shulcloper, Weighted cluster ensemble using a kernel consensus function, LNAI, vol. 5197, 2008, pp. 195–202. 19. [19] Wang, X., Yang, C., Zhou, J., Clustering aggregation by probability accumulation. Pattern Recognit. 42:5 (2009), 668–675. 20. [20] Al-razgan, M., Domeniconi, C., Weighted cluster ensembles: methods and analysis. ACM Trans. Knowl. Discov. Data 2:4 (2009), 17–40 (17:1). 21. [21] N. Iam-On, T. Boongoen, S. Garrett, Refining pairwise similarity matrix for cluster ensemble problem with cluster relations, Discovery Science, LNAI, vol. 5255, 2008, pp. 222–233. 22. [22] Zhong, C., Yue, X., Zhang, Z., Lei, J., A clustering ensemble: two-level-refined co-association matrix with path-based transformation. Pattern Recognit. 48:8 (2015), 2699–2709. 23. [23] Berikov, V., A latent variable pairwise classification model of a clustering ensemble. Sansone, C., Kittler, J., Roli, F., (eds.) Multiple Classifier Systems, 2011, Lecture Notes on Computer Science, 6713, 2011, Springer, Heidelberg, 279–288. 24. [24] Berikov, V., Weighted ensemble of algorithms for complex data clustering. Pattern Recognit. Lett. 38 (2014), 99–106. 25. [25] Ablin, R., Sulochana, C.H., A survey of hyperspectral image classification in remote sensing. Int. J. Adv. Res. Comput. Commun. Eng. 2:8 (2013), 2986–3003. 26. [26] 〈 http://alweb.ehu.es/ccwintco/index.php?Title=Hyperspectral_Remote_Sensing_Scenes 〉. 27. [27] N.A. Weiss, A Course in Probability, 2005.