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JACIII Vol.12 No.5 pp. 448-453
doi: 10.20965/jaciii.2008.p0448
(2008)

Paper:

Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering

Sadaaki Miyamoto*, Youhei Kuroda, and Kenta Arai

*Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba
Ibaraki 305-8573, Japan

Received:
October 10, 2007
Accepted:
February 15, 2008
Published:
September 20, 2008
Keywords:
possibilistic clustering, sequential extraction of clusters, mountain clustering
Abstract

In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering’s similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.

Cite this article as:
Sadaaki Miyamoto, Youhei Kuroda, and Kenta Arai, “Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.5, pp. 448-453, 2008.
Data files:
References

    [1] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum, New York, 1981.
    [2] J. C. Bezdek, J. Keller, R. Krishnapuram, and N. R. Pal, “Fuzzy Models and Algorithms for Pattern Recognition and Image,” Proc. Kluwer, Boston, 1999.
    [3] F. Höppner, F. Klawonn, R. Kruse, and T. Runkler, “Fuzzy Cluster Analysis,” Wiley, Chichester, 1999.
    [4] R. Krishnapuram and J. M. Keller, “A possibilistic approach to clustering,” IEEE Trans. on Fuzzy Syst, Vol.1 , No.2, pp. 98-110, 1993.
    [5] R. N. Davé and R. Krishnapuram, “Robust clustering methods: a unified view,” IEEE Trans. Fuzzy Syst, Vol.5, No.2, pp. 270-293, 1997.
    [6] R. R. Yager and D. Filev, “Approximate clustering via the mountain method,” IEEE Trans., on Syst, Man, and Cybern, Vol.24, No.8, pp. 1279-1284, 1994.
    [7] S. Miyamoto and M. Mukaidono, “Fuzzy c-means as a regularization and maximum entropy approach,” Proc. of the 7th Int. Fuzzy Systems Association World Congress (IFSA'97), June 25-30, 1997, Prague, Czech, Vol.II, pp. 86-92, 1997.
    [8] J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” J. of Cybernetics, Vol.3, pp. 32-57, 1974.
    [9] T. A. Runkler and C. Katz, “Fuzzy Clustering by Particle Swarm Optimization,” 2006 IEEE Int. Conf. on Fuzzy Systems, Vancouver, BC, Canada, pp. 3065-3072, July 16-21, 2006.
    [10] L. Kaufman and P. J. Rousseeuw, “Finding Groups in Data: An Introduction to Cluster Analysis,” Wiley, 1990.
    [11] S. Miyamoto, R. Inokuchi, and Y. Kuroda, “Possibilistic and Fuzzy c-Means Clustering with Weighted Objects,” Proc. of 2006 IEEE Int. Conf. on Fuzzy Systems, Vancouver, BC, Canada, pp. 4260-4265, July 16-21, 2006.
    [12] H. Ichihashi, K. Miyagishi, and K. Honda, “Fuzzy c-means clustering with regularization by K-L information,” Proc. of 10th IEEE Int. Conf. on Fuzzy Systems, Vol.2, pp. 924-927, 2001.

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Last updated on Jun. 08, 2021