Comparison and Evaluation of Different Cluster Validity Measures Including Their Kernelization
Wataru Hashimoto, Tetsuya Nakamura, and Sadaaki Miyamoto
Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan
-  J.C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum, New York, 1981.
-  F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, “Fuzzy Cluster Analysis,” Wiley, Chichester, 1999.
-  M. Girolami, “Mercer kernel based clustering in feature space,” IEEE Trans. on Neural Networks, Vol.13, No.3, pp. 780-784, 2002.
-  S. Miyamoto and D. Suizu, “Fuzzy c-means clustering using kernel functions in support vector machines,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.7, No.1, pp. 25-30, 2003.
-  C.J.C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, Vol.2, pp. 121-167, 1998.
-  V. N. Vapnik, “Statistical Learning Theory,” Wiley, New York, 1998.
-  J.C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” J. of Cybernetics, Vol.3, pp. 32-57, 1974.
-  D. Dumitrescu, B. Lazzerini, and L.C. Jain, “Fuzzy Sets and their Application to Clustering and Training,” CRC Press, Boca Ration, Florida, 2000.
-  I. Gath and A.B. Geva, “Unsupervised optimal fuzzy clustering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.11, No.7, pp. 773-781, 1989.
-  X.L. Xie and G. Beni, “A Validity measure for Fuzzy Clustering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.13, No.4, pp. 841-846, 1991.
-  Y. Fukuyama and M. Sugeno, “A new method for choosing the number of clusters for the fuzzy c-means method,” Proc. 5th Fuzzy System Symposium. pp. 247-250, July 1989.
-  D. L. Davies and D. W. Bouldin, “Cluster Separation Measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.1, No.2, pp. 95-104, 1979.
-  S. Miyamoto and Y. Nakayama, “Algorithms of hard c-means clustering using kernel functions in support vector machines,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.7, No.1, pp. 19-24, 2003.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.