Abstract
The fuzzy clustering scenario resulting from Fuzzy c-Means Algorithm (FCM) is highly sensitive to input parameters, number of clusters c and randomly initialized fuzzy membership matrix. Traditionally, the optimal fuzzy clustering scenario is arrived by exhaustive, repetitive invocations of FCM for different c values. In this paper, a new preprocessor based on Simplified Fuzzy Min-Max Neural Network (FMMNN) is proposed for FCM. The new preprocessor results in an estimation of the number of clusters in a given dataset and an initialization for fuzzy membership matrix. FCM algorithm with embeded preprocessor is named as FCMPre. Comparative experimental results of FCMPre with FCM based on benchmark datasets, empirically established that FCMPre discovers optimal (or near optimal) fuzzy clustering scenarios without exhaustive invocations of FCM along with obtaining significant computational gains.
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
Pal, N.R., Bezdek, J.C.: On Cluster Validity for the Fuzzy C-means Model. Trans. Fuz. Sys. 3, 370–379 (1995)
Wang, W., Zhang, Y.: On fuzzy cluster validity indices. Fuzzy Sets and Systems 158, 2095–2117 (2007)
Dunn, J.C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Cybernetics and Systems 3, 32–57 (1973)
Ma, D., Liu, J., Wang, Z.: The Pattern Classification Based on Fuzzy Min-max Neural Network with New Algorithm. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds.) ISNN 2012, Part II. LNCS, vol. 7368, pp. 1–9. Springer, Heidelberg (2012)
Simpson, P.K.: Fuzzy min-max neural networks. In: 1991 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 1658–1659 (1991)
Goktepe, A.B., Altun, S., Sezer, A.: Soil clustering by Fuzzy c-Means algorithm 36, 691–697 (2005)
Chuang, K.-S., Tzeng, H.-L., Chen, S., Wu, J., Chen, T.-J.: Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics 30, 9–15 (2006)
Phillips II, W.E., Velthuizen, R.P., Phuphanich, S., Hall, L.O., Clarke, L.P., Silbiger, M.L.: Application of Fuzzy c-Means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magnetic Resonance Imaging 13, 277–290 (1995)
Hathaway, R.J., Bezdek, J.C.: Fuzzy C-means Clustering of Incomplete Data. Trans. Sys. Man Cyber. Part B 31, 735–744 (2001)
Dembl, D., Kastner, P.: Fuzzy C-means method for clustering microarray data. Bioinformatics 19, 973–980 (2003)
Li, X., Lu, X., Tian, J., Gao, P., Kong, H., Xu, G.: Application of Fuzzy c-Means Clustering in Data Analysis of Metabolomics. Analytical Chemistry 81, 4468–4475 (2009)
Eschrich, S., Ke, J., Hall, L.O., Goldgof, D.B.: Fast accurate fuzzy clustering through data reduction. IEEE Transactions on Fuzzy Systems 11, 262–270 (2003)
Wang, X., Wang, Y., Wang, L.: Improving Fuzzy c-Means clustering based on feature-weight learning. Pattern Recognition Letters 25, 1123–1132 (2004)
Cheng, T.W., Goldgof, D.B., Hall, L.O.: Fast fuzzy clustering. Fuzzy Sets and Systems 93, 49–56 (1998)
Al-Zoubi, M.B., Hudaib, A., Al-Shboul, B.: A Fast Fuzzy Clustering Algorithm. In: Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, vol. 3, pp. 28–32 (2007)
Bezdek, J., Ehrlich, R., Full, W.: FCM: The Fuzzy c-Means clustering algorithm. Computers & Geosciences 10, 191–203 (1984)
Simpson, P.K.: Fuzzy min-max neural networks - Part 2: Clustering. IEEE Transactions on Fuzzy Systems 1 (1993)
Nguyen, D.T., Doan, H.: An Approach to Determine the Number of Clusters for Clustering Algorithms. In: Nguyen, N.-T., Hoang, K., Jędrzejowicz, P. (eds.) ICCCI 2012, Part I. LNCS (LNAI), vol. 7653, pp. 485–494. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
S., R., Sai Prasad, P.S.V.S., Chillarige, R.R. (2014). A New Preprocessor to Fuzzy c-Means Algorithm. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2014. Lecture Notes in Computer Science(), vol 8875. Springer, Cham. https://doi.org/10.1007/978-3-319-13365-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-13365-2_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13364-5
Online ISBN: 978-3-319-13365-2
eBook Packages: Computer ScienceComputer Science (R0)