A generalized fuzzy-possibilistic c-means clustering algorithm

[1] S. Aeberhard, M. Floina, Wine, UCI Machine Learning Repository (1991) ⇒414, 415 Search in Google Scholar

[2] R. Andersen, Irises of the Gaspe Peninsula, Bull. Amer, Iris Soc. 59 (1935) 2–5. ⇒415 Search in Google Scholar

[3] J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Plenum, New York (1981) ⇒405 Search in Google Scholar

[4] H. Choe, J.B. Jordan, On the optimal choice of parameters in a fuzzy c-means algorithm, IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), 1992, pp. 349–354. ⇒429 Search in Google Scholar

[5] R.N. Davé, Characterization and detection of noise in clustering. Pattern Recognition Letters, 12, 11 (1991) 657–664. ⇒405 Search in Google Scholar

[6] J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters, J. Cybern. 3, 3 (1974) 32–57. ⇒405 Search in Google Scholar

[7] B. Everitt, Cluster analysis, Chichester, West Sussex, U.K. (2011) ⇒414 Search in Google Scholar

[8] R.A. Fisher, Iris, UCI Machine Learning Repository (1988) ⇒414, 415 Search in Google Scholar

[9] R. Krishnapuram, J.M. Keller, A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems 1, 2 (1993) 98–110. ⇒405, 408, 426 Search in Google Scholar

[10] T. O. Kvålseth, On normalized mutual information: Measure derivations and properties, Entropy 19, 11 (2017) 613–114. ⇒414 Search in Google Scholar

[11] N.R. Pal, K. Pal, J.M. Keller, J.C. Bezdek, A mixed c-means clustering model, IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), 1997, pp. 11–21. ⇒405, 410 Search in Google Scholar

[12] N.R. Pal, K. Pal, J.M. Keller, J.C. Bezdek, A possibilistic fuzzy c-means clustering algorithm, IEEE Transactions on Fuzzy Systems 13, 4 (2005) 517–530. ⇒406, 410 Search in Google Scholar

[13] W. Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association 66, 336 (1971) 846–850. ⇒414 Search in Google Scholar

[14] T.A. Runkler, A Convergence Study of the Possibilistic One Means Algorithm, IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), 2023, art. no. 10309756, pp. 1–6. ⇒413 Search in Google Scholar

[15] E,H. Ruspini, A new approach to clustering Information and Control 15, 1 (1969) 22–32. ⇒405 Search in Google Scholar

[16] L. Szilágyi, Fuzzy-Possibilistic Product Partition: a novel robust approach to c-means clustering, Lect. Notes Comput Sci. 6820 (2011) 150–161. ⇒406, 410 Search in Google Scholar

[17] L. Szilágyi, Robust spherical shell clustering using fuzzy-possibilistic product partition Int. J. Intell. Syst. 28, 6 (2013) 524–539. ⇒410 Search in Google Scholar

[18] L. Szilágyi, S. M. Szilágyi, Generalization rules for the suppressed fuzzy c-means clustering algorithm, Neurocomput. 139 (2014) 298–309. ⇒417 Search in Google Scholar

[19] L. Szilágyi, Zs.R. Varga, S.M. Szilágyi, Application of the fuzzy-possibilistic product partition in elliptic shell clustering, Lect. Notes Comput Sci. 8825 (2014) 158–169. ⇒410 Search in Google Scholar

[20] V. Torra, On the selection of m for Fuzzy c-Means, Conference of the International Fuzzy Systems Association, 2015, pp. 1571–1577. ⇒420, 429 Search in Google Scholar

[21] W. Wolberg, O. Mangasarian, N. Street, W. Street, Breast Cancer Wisconsin (Diagnostic), UCI Machine Learning Repository (1995) ⇒414, 415 Search in Google Scholar

[22] K. L. Wu, Analysis of parameter selections for fuzzy c-means, Pattern Recognition 45, 1 (2012) 407–415. ⇒429 Search in Google Scholar

[23] X. L. Xie, G. A. Beni, A validity measure for fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell. 13, 8 (1991) 841–847. ⇒417 Search in Google Scholar

[24] L. Zadeh, Fuzzy sets, Information and Control 8, (1965) 338–353. ⇒405 Search in Google Scholar