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An Improved Clustering Method with Cluster Density Independence
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 Title & Authors
An Improved Clustering Method with Cluster Density Independence
Yoo, Byeong-Hyeon; Kim, Wan-Woo; Heo, Gyeongyong;
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 Abstract
In this paper, we propose a modified fuzzy clustering algorithm which can overcome the center deviation due to the Euclidean distance commonly used in fuzzy clustering. Among fuzzy clustering methods, Fuzzy C-Means (FCM) is the most well-known clustering algorithm and has been widely applied to various problems successfully. In FCM, however, cluster centers tend leaning to high density clusters because the Euclidean distance measure forces high density cluster to make more contribution to clustering result. Proposed is an enhanced algorithm which modifies the objective function of FCM by adding a center-scattering term to make centers not to be close due to the cluster density. The proposed method converges more to real centers with small number of iterations compared to FCM. All the strengths can be verified with experimental results.
 Keywords
Clustering;FCM;Cluster density;Density independence;
 Language
Korean
 Cited by
 References
1.
J. Bezdek, Pattern Recognition with fuzzy Objective Function Algorithms, New York, Springer, January 1981.

2.
Sadaaki Muyamoto, Fuzzy Clustering - Basic Ideas and Overview, Handbook of Computational Intelligence, Springer, pp. 293-248, May 2015.

3.
Janmenjoy Nayak, Fuzzy C-means(FCM) Clustering Algorithm: A Decade Review from 2000 to 2014, Systems and Technologies, Vol. 32, No. 2, pp. 133-179, December 2014.

4.
Zarita Zainuddin, An effective Fuzzy C-Means algorithm based on symmetry similarity approach, Applied Soft Computing, Vol. 35, No. 10, pp. 433-448, October 2015. crossref(new window)

5.
Basel Abu-Jamous, Fuzzy Clustering, Integrative Cluster Analysis in Bioinformatics, Chapter 13, April 2015.

6.
G. Heo, An Extension of Possibilistic Fuzzy C-Means using Regularization, Journal of the Korean Society of Computer Information, Vol. 15, No. 1, pp. 43-50, January 2010. crossref(new window)

7.
R. N. Dave, Characterization and detection of noise in clustering, Pattern Recognition Letters, Vol. 12, No. 11, pp. 657-664, November 1991. crossref(new window)

8.
R. Babuska, P. J. van der Veen and U Kaymak, Improved Covariance Estimation for Gustafson-Kessel Clustering, Proceeding of the 2002 IEEE International Conference on Fuzzy Systems, pp. 1081-1085, May 2002.

9.
G. Heo, Extension of the Possibilistic Fuzzy C-Means Clustering Algorithm, Proceedings of KFIS Autumn Conference, Vol. 17, No. 2, November 2007.

10.
I. Gath and A. B. Geva, Unsupervised Optimal Fuzzy Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 7, pp. 773-791, July 1989. crossref(new window)