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Clustering Method for Reduction of Cluster Center Distortion

클러스터 중심 왜곡 저감을 위한 클러스터링 기법

  • Published : 2008.06.25

Abstract

Clustering is a method to classify the given data set with same property into several classes. To cluster data, many methods such as K-Means, Fuzzy C-Means(FCM), Mountain Method(MM), and etc, have been proposed and used. But the clustering results of conventional methods are sensitively influenced by initial values given for clustering in each method. Especially, FCM is very sensitive to noisy data, and cluster center distortion phenomenon is occurred because the method dose clustering through minimization of within-clusters variance. In this paper, we propose a clustering method which reduces cluster center distortion through merging the nearest data based on the data weight, and not being influenced by initial values. We show the effectiveness of the proposed through experimental results applied it to various types of data sets, and comparison of cluster centers with those of FCM.

Keywords

Cluster ;center distortion;FCM;Nearest data;Data weight

References

  1. 이중우, 손세호, 권순학, "개선된 산 클러스터링 방법," 한국 퍼지 및 지능시스템 학회 논문지, 제 11권, 1호, pp. 1-8, 2001
  2. J. S. Nath, S. K. Shevade, "An efficient clustering scheme using support vector methods," Pattern Recognition, Vol. 36, No. 8, pp. 1473-1480, 2006
  3. T. Hu, Y. Yu, J. Xiong, S. Y. Sung, "Maximum likelihood combination of multiple clusterings," Pattern Recognition Letters, Vol. 27, pp. 1457-1464, 2006 https://doi.org/10.1016/j.patrec.2006.02.013
  4. A. M. Bensaid, L. O. Hall, J. C. Bezdek, L. P. Clarke, M. L. Silbiger, J. A. Arrington, R. F. Murtagh, "Validity-guided clustering with applications to image (re)segmentation," IEEE Trans. Fuzzy Systerms, Vol. 4, No. 2, pp. 112-123. 1996 https://doi.org/10.1109/91.493905
  5. J. C. Dunn, "Indices of partition fuzziness and the detection of clusters in large data sets," Fuzzy Automata and Decision Processes, M. M. Gupta, Ed. Elsvier, New York, 1976
  6. J. A. Hartigan, M. A. Wong, "A K-means clustering algorithm," Applied Statistics, Vol. 28, pp. 100-108, 1979 https://doi.org/10.2307/2346830
  7. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Pleum, New York, 1981
  8. K. Blekas, I. E. Lagaris, "Newtonian clustering: An approach based on molecular dynamics and global optimization," Pattern Recognition, Vol. 40, No. 6, pp. 1734-1744, 2007 https://doi.org/10.1016/j.patcog.2006.07.012
  9. H. Wang, C. Wang, G. Wu, "Bi-criteria fuzzy C-means analysis," Fuzzy Sets and Systems, Vol. 64, pp. 311-319, 1994 https://doi.org/10.1016/0165-0114(94)90154-6
  10. R. R. Yager and D. P. Filev, Essential of fuzzy modeling and control, John Wiley & Sons, Inc., New York, 1994
  11. S. Jiang, X. Song, H. Wang, J. J. Han, Q. H. Li, "A clustering-based method for unsupervised intrusion detections," Pattern Recognition Letters, Vol. 27, pp. 802-810, 2006 https://doi.org/10.1016/j.patrec.2005.11.007
  12. K. L. Wu, M. S. Yang, "Alternative C-means clustering algorithms," Pattern Recognition, Vol. 35, pp. 2267-2278, 2002 https://doi.org/10.1016/S0031-3203(01)00197-2
  13. H. Rhee, K. Oh, "A design and analysis of objective function-based unsupervised neural networks for fuzzy clustering," Neural Processing Letters, Vol. 4, pp. 82-95, 1996
  14. S. H. Kwon, "Cluster validity index for fuzzy clustering," Electronics Letters, Vol. 34, No. 22, pp. 2176-2177, 1998 https://doi.org/10.1049/el:19981523

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