Abstract
A new generalized clustering algorithm with the name of genetic algorithm based rough-fuzzy possibilistic c-means (GARFPCM) is proposed. It derives from an unsupervised learning algorithm called RFPCM, which is unstable for the reason of random initialization. GA is introduced into RFPCM to generate an improved version, which is GARFPCM mentioned above. GARFPCM can obtain better clustering quality. Through performance evaluation on image segmentation, GARFPCM is shown to perform excellently.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jain, A.K., Murty, M.N., Flyn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3) (1999)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithm. Plenum, New York (1981)
Krishnapuram, R., Keller, J.M.: The Possibilistic C-Means Algorithm: Insights and Recommandations. IEEE Trans. Fuzzy Syst. 4, 385–393 (1996)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Dordrecht (1991)
Maji, P., Pal, S.K.: Rough Set Based Generalized Fuzzy C-Means Algorithm and Quantitative Indices. IEEE Trans. Syst., Man, Cybern.-Part B: Cybernetics 37(6) (2007)
Hruschka, E.R., Campello, R.J.B., Freitas, A.A., de Carvalho, A.C.P.L.F.: A Survey of Evolutionary Algorithms for Clustering. IEEE Trans. Syst., Man, Cybern.-Part C: Applications and Reviews 39(2) (2009)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Pal, N.R., Pal, S.K.: A Review on Image Segmentation Techniques. Pattern Recognition 26(9), 1277–1294 (1993)
Fu, K.S., Mui, J.K.: A Survey on Image Segmentation. Pattern Recognition 13(1), 3–16 (1981)
Bezdek, J.C., Hall, L.O., Clarke, L.P.: Review of MR Image Segmentation Techniques Using Pattern Recognition. Medical Physics 20(4), 1033–1048 (1993)
Pappas, T.: An Adaptive Clustering Algorithm for Image Segmentation. IEEE Trans. Signal Process. 40(4), 901–914 (1992)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Images Classification. IEEE Trans. Syst., Man, Cybern. 3(6), 610–621 (1973)
Fukuda, S., Hirosawa, H.: A Wavelet-Based Texture Feature Set Applied to Classification of Multifrequency Polarimetric SAR Images. IEEE Trans. Geosci. Remote Sensing 37(8), 2282–2286 (1999)
Srinivasan, G.N., Shobha, G.: Statistical Texture Analysis. Proc. of Word Academy of Science, Engineering and Technology 36, 1264–1270 (2008)
Clausi, D.A., Yue, B.: Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery. IEEE Trans. Geosci. Remote Sensing 42(1), 215–228 (2004)
Jiao, L., Gong, M., et al.: Natural and Remote Sensing Image Segmentation Using Memetic Computing. IEEE Computational Intelligence Magazine 5(2), 78–91 (2010)
Wang, D.: A Multiscale Gradient Algorithm for Image Segmentation Using Watersheds. Pattern Recognition 30(12), 2043–2052 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, W., Ge, X., Jiao, L. (2012). An Improved Generalized Fuzzy C-Means Clustering Algorithm Based on GA. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-31919-8_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
eBook Packages: Computer ScienceComputer Science (R0)