Abstract
Iterative fuzzy clustering algorithms are sensitive to initialization. Swarm based clustering algorithms are able to do a broader search for the best extrema. A swarm inspired clustering approach which searches in fuzzy cluster centroids space is discussed. An evaluation function based on fuzzy cluster validity was used. A swarm based clustering algorithm can be computationally intensive and a data distributed approach to clustering is shown to be effective. It is shown that the swarm based clustering results in excellent data partitions. Further, it shown that the use of a cluster validity metric as the evaluation function enables the discovery of the number of clusters in the data in an automated way.
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References
BEZDEK, J.C., KELLER, J., KRISHNAPURAM, R., and PAL, N. (1999): Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer, Boston, MA.
BLAKE, C.L., and MERZ, C.J. (1998): UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html.
HALL, L.O., and KANADE, P.M. (2005): Swarm based fuzzy clustering with partition validity. Proc. 14th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’05). IEEE Press, Piscataway, NJ. To Appear.
HANDL, J., KNOWLES, J., and DORIGO, M. (2003a): On the performance of antbased clustering. design and application of hybrid intelligent systems. Frontiers in Artificial intelligence and Applications 104, 204–213.
HANDL, J., KNOWLES, J., and DORIGO, M. (2003b): Strategies for the increased robustness of ant-based clustering. Self-Organising Applications: Issues, challenges and trends, LNCS 2977, 90–104. Springer-Verlag, Berlin.
HATHAWAY, R.J., and BEZDEK, J.C. (1995): Optimization of clustering criteria by reformulation. IEEE Trans. on Fuzzy Systems, 3(2):241–245. IEEE Press, Piscataway, NJ.
HORE, P. (2004): Distributed clustering for scaling classic algorithms. Master’s thesis, University of South Florida, Tampa, FL.
HORE, P., and HALL, L.O. (2004): Distributed clustering for scaling classic algorithms. Proc. 13th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’04). IEEE Press, Piscataway, NJ.
KANADE, P. (2004): Fuzzy ants as a clustering concept. Master’s thesis, University of South Florida, Tampa, FL.
KANADE, P.M., and HALL, L.O. (2004): Fuzzy ants clustering with centroids. Proc. 13th IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE’04). IEEE Press, Piscataway, NJ.
KIM, D.W., LEE, K.H., and LEE, D. (2004): A novel initialization scheme for the fuzzy c-means algorithm for color clustering. Pattern Recognition Letters, 25(2):227–237.
LABROCHE, N., MONMARCHE, N., and VENTURINI, G. (2002): A new clustering algorithm based on the chemical recognition system of ants. Proc. European Conf. on Artificial Intelligence, 345–349.
MONMARCHÉ, N., SLIMANE, M., and VENTURINI, G. (1999): On improving clustering in numerical databases with artificial ants. Proc. 5th European Conf. on Artificial Life (ECAL’99), LNAI 1674, 626–635. Springer-Verlag, Berlin.
OUADFEL, S., and BATOUCHE, M. (2002): Unsupervised image segmentation using a colony of cooperating ants. Biologically Motivated Computer Vision, 2nd Int. Workshop, BMCV 2002, LNCS 2525, 109–116. Springer-Verlag, Berlin.
PAL, N.R., and BEZDEK, J.C. (1995): On cluster validity for the fuzzy c-means model. IEEE Trans. on Fuzzy Systems, 3(3):370–379.
ULTSCH, A. (2004): Strategies for an artificial life system to cluster high dimensional data. Abstracting and Synthesizing the Principles of Living Systems, GWAL-6, 128–137.
XIE, X.L., and BENI, G.A. (1991): Validity measure for fuzzy clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 3(8):841–846.
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Hall, L.O., Kanade, P.M. (2006). Scalable Swarm Based Fuzzy Clustering. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_3
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DOI: https://doi.org/10.1007/3-540-31314-1_3
Publisher Name: Springer, Berlin, Heidelberg
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