Similarity Measures and Weighted Fuzzy C-Mean Clustering Algorithm
In this paper we study the fuzzy c-mean clustering algorithm combined with principal components method. Demonstratively analysis indicate that the new clustering method is well rather than some clustering algorithms. We also consider the validity of clustering method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1330571Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1481
 V.V. Cross and T.A. Sudkamp, Similarity and Compatibility in Fuzzy Set Theory: assessment and Applications, Physica-Verlag, New York, 2002.
 M. Kalina, Derivatives of fuzzy functions and fuzzy derivatives, Tatra Mountains Mathematical Publications 12 (1997) 27-34.
 K.L.Wu and M.S.Yang. Alternative c-means clustering algorithms. Pattern Recognition. 2001,120:249-254.
 I.Berget, B.H.Mevi and T.Nas. New modifications and applications of fuzzy c-means methodology. Computational Statistics & Data Analysis. 2008,52:2403-2418.
 X.Z.Wang, Y.D.Wang and L.J.Wang. Improving fuzzy c-means clustering based on feature-weighted learning. Pattern Recognition Letters.2004, 25:1123-1132.
 K.S.Zhang, B.N.Li. New modification of fuzzy c-means clustering algorithm. In Cao BY, Zhang CY Proceedings of the Third Annual Conference on Fuzzy Information and Engineering .New York: Springer, 2009: 448- 455.
 W.L.Hung, M.S.Yang and D.H.Chen. Bootstrapping approach to featureweight selection in fuzzy c-means algorithms with an application in color image segmentation. Pattern Recognition Letters,2008,29:1317-1325.
 J.J.Higgings. Introduction to Modern Nonparametric Statistics. Duxbury, Belmont, CA,2002.
 K.J.Zhu, S.H.Shu and J.L. Li. Optimal number of clusters and the best partion in fuzzy c-means. Systems, Engineering-Theory and Practice. 2005, 3:52-61.(in Chinese)
 Y.J.Zhang, W.N.Wang, X.N. Zhang and Y.Li. A cluster validity index for fuzzy clustering. Information Sciences. 2008,178:1205-1218.