Abstract
Suppressed fuzzy c-means (s-FCM) clustering was introduced with the intention of combining the higher convergence speed of hard c-means (HCM) clustering with the finer partition quality of fuzzy c-means (FCM) algorithm. Suppression modifies the FCM iteration by creating a competition among clusters: lower degrees of memberships are reduced via multiplication with a previously set constant suppression rate, while the largest fuzzy membership grows by swallowing all the suppressed parts of the small ones. Suppressing the FCM algorithm was found successful in terms of accuracy and working time. In this paper we introduce some generalized formulations of the suppression rule, leading to an infinite number of new clustering algorithms. Based on a large amount of numerical tests performed in multidimensional environment, some generalized forms of suppression proved to give more accurate partitions than FCM and s-FCM.
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References
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/MLRepository.html
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, New York (1981)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybern. 3, 32–57 (1974)
Fan, J.L., Zhen, W.Z., Xie, W.X.: Suppressed fuzzy c-means clustering algorithm. Patt. Recogn. Lett. 24, 1607–1612 (2003)
Hathaway, R.J., Bezdek, J.C., Hu, Y.: Generalized fuzzy c-means clustering strategies using L p norm distances. IEEE Trans. Fuzzy Syst. 8, 576–582 (2000)
Hung, W.L., Yang, M.S., Chen, D.H.: Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Patt. Recogn. Lett. 27, 424–438 (2006)
Kamel, M.S., Selim, S.Z.: New algorithms for solving the fuzzy clustring problem. Patt. Recogn. 27, 421–428 (1994)
Ruspini, E.H.: A new approach to clustering. Inform. Contr. 16, 22–32 (1969)
Szilágyi, L., Szilágyi, S.M., Benyó, Z.: Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models. Soft. Comput. 14, 495–505 (2010)
Tsao, E.C.K., Bezdek, J.C., Pal, N.R.: Fuzzy Kohonen clustering networks. Patt. Recogn. 27, 757–764 (1994)
Xie, Z., Wang, S., Chung, F.L.: An enhanced possibilistic c-means clustering algorithm. Soft Comput. 12, 593–611 (2008)
Yair, E., Zeger, K., Gersho, A.: Competitive learning and soft competition for vector quantization design. IEEE Trans. Sign. Proc. 40, 294–309 (1992)
Zadeh, L.A.: Fuzzy sets. Inform. Contr. 8, 338–353 (1965)
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Szilágyi, L., Szilágyi, S.M., Kiss, C. (2010). A Generalized Approach to the Suppressed Fuzzy c-Means Algorithm. In: Torra, V., Narukawa, Y., Daumas, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2010. Lecture Notes in Computer Science(), vol 6408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16292-3_15
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DOI: https://doi.org/10.1007/978-3-642-16292-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16291-6
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