A Fixed Suppressed Rate Selection Method for Suppressed Fuzzy C-Means Clustering Algorithm

Jiulun Fan, Jing Li

School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, China.
School of Electronic Engineering, Xidian University, Xi’an, China.

DOI: 10.4236/am.2014.58119                

Abstract

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the fixed suppressed rate by the structure of the data itself. The experimental results show that the proposed method is a suitable way to select the suppressed rate in suppressed fuzzy c-means clustering algorithm.

Keywords

Hard C-Means Clustering Algorithm, Fuzzy C-Means Clustering Algorithm, Suppressed Fuzzy C-Means Clustering Algorithm, Suppressed Rate

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York. http://dx.doi.org/10.1007/978-1-4757-0450-1
[2]	Yu, J. and Yang, M.-S. (2005) Optimality Test for Generalized FCM and Its Application to Parameter Selection. IEEE Transactions on Fuzzy Systems, 13, 164-176. http://dx.doi.org/10.1109/TFUZZ.2004.836065
[3]	Cannon, R.L., Dave, J.V. and Bezdek, J.C. (1986) Efficient Implementation of the Fuzzy c-Means Clustering Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, 248-255. http://dx.doi.org/10.1109/TPAMI.1986.4767778
[4]	Hathaway, R.J. and Bezdek, J.C. (1995) Optimization of Clustering Criteria by Reformulation. IEEE Transactions on Fuzzy Systems, 3, 241-245. http://dx.doi.org/10.1109/91.388178
[5]	Kolen, J.F. and Hutcheson, T. (2002) Reducing the Time Complexity of the Fuzzy c-Means Algorithm. IEEE Transactions on Fuzzy Systems, 10, 263-267. http://dx.doi.org/10.1109/91.995126
[6]	Kwok, T., Smith, K., Lozano, S. and Taniar, D. (2002) Parallel Fuzzy c-Means Clustering for Large Data Sets. EuroPar 2002 Parallel Processing, Springer, Berlin, 365-374. http://dx.doi.org/10.1007/3-540-45706-2_48
[7]	Lázaro, J., Arias, J., Martín, J., Cuadrado, C. and Astarloa, A. (2005) Implementation of a Modified Fuzzy c-Means Clustering Algorithm for Real-Time Applications. Microprocessors and Microsystems, 29, 375-380. http://dx.doi.org/10.1016/j.micpro.2004.09.002
[8]	Cheng, T.W., Goldgof, D.B. and Hall, L.O. (1998) Fast Fuzzy Clustering. Fuzzy Sets and Systems, 93, 49-56. http://dx.doi.org/10.1016/S0165-0114(96)00232-1
[9]	Fan, J., Zhen, W. and Xie, W. (2003) Suppressed Fuzzy C-Means Clustering Algorithm. Pattern Recognition Letters, 24, 1607-1612. http://dx.doi.org/10.1016/S0167-8655(02)00401-4
[10]	Szilágyi, L., Szilágyi, S.M. and Benyo, Z. (2010) Analytical and Numerical Evaluation of the Suppressed Fuzzy c-Means Algorithm: A Study on the Competition in c-Means Clustering Models. Soft Computing, 14, 495-505. http://dx.doi.org/10.1007/s00500-009-0452-y
[11]	Szilágyi, L., Szilágyi, S.M. and Benyo, Z. (2008) Analytical and Numerical Evaluation of the Suppressed Fuzzy c-Means Algorithm. Lecture Notes in Computer Science, 5285, 146-157. http://dx.doi.org/10.1007/978-3-540-88269-5_14
[12]	Szilágyi, L., Szilágyi, S.M. and Benyo, Z. (2008) A Thorough Analysis of the Suppressed Fuzzy C-Means Algorithm, Progress in Pattern Recognition, Image Analysis and Applications. Lecture Notes in Computer Science, 5197, 203-210. http://dx.doi.org/10.1007/978-3-540-85920-8_25
[13]	Hung, W.L., Yang, M.S. and Chen, D.H. (2006) Parameter Selection for Suppressed Fuzzy C-Means with an Application to MRI Segmentation. Pattern Recognition Letters, 27, 424-438. http://dx.doi.org/10.1016/j.patrec.2005.09.005
[14]	Hung, W.-L. and Chang, Y.-C. (2006) A Modified Fuzzy C-Means Algorithm for Differentiation in MRI of Ophthalmology, Modeling Decisions for Artificial Intelligence. Lecture Notes in Computer Science, 3885, 340-350. http://dx.doi.org/10.1007/11681960_33
[15]	Nyma, A., Kang, M., Kwon, Y.-K., Kim, C.-H. and Kim, J.-M. (2012) A Hybrid Technique for Medical Image Segmentation. Journal of Biomedicine and Biotechnology, 2012, Article ID: 830252. http://dx.doi.org/10.1155/2012/830252
[16]	Li, Y. and Li, G. (2010) Fast Fuzzy c-Means Clustering Algorithm with Spatial Constraints for Image Segmentation, Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, 67, 431-438. http://dx.doi.org/10.1007/978-3-642-12990-2_49
[17]	Saad, M.F. and Alimi, A.M. (2010) Improved Modified Suppressed Fuzzy C-Means. 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, 7-10 July 2010, 313-318.
[18]	http://www.ics.uci.edu/