A Non-linear Variant of Global Clustering Using Kernel Methods

허경용; 김성훈; Woo, Young Woon

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2010, 15(4), pp.11-18
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

허경용 ¹, 김성훈 ², Woo, Young Woon ³

¹University of Florida
²경북대학교
³동의대학교

ABSTRACT

Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.
KEYWORDS

Fuzzy Clustering, Global Clustering, Cluster Initialization, Kernel Clustering, Non-linear Clustering
REFERENCES (12)

[journal] R. Xu / 2005 / Survey of clustering algorithms / IEEE Transactions on Neural Networks 16(3) : 645~678

[confproc] W. Wang / 2006 / The global fuzzy c-means clustering algorithm / Proceedings of the 6th World Congress on Intelligent Control and Automation : 3604~3607

[thesis] H.-S.Tsai / 2007 / A study on kernel-based clustering algorithms / Ph.D / Chung Yuan Christian University

[confproc] J. He / 2004 / Initialization of cluster refinement algorithms: A review and comparative study / Proceedings of the 2004 IEEE International Joint Conference on Neural Networks : 297~302

[journal] A.Likas / 2003 / The global k-means clustering algorithm / Pattern Recognition 36(2) : 451~461

[journal] A.M.Bagirov / 2008 / Modified global k-means algorithm for minimum sum-of-squares clustering problems / Pattern Recognition 41(10) : 3192~3199

[confproc] J.B.MacQueen / 1966 / Some methods for classification and analysis of multivariate observations / Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability,University of California Press,Berkeley,CA, : 281~297

[journal] R. J. Hathaway / 1995 / Optimization of clustering criteria by reformulation / IEEE Transactions on Fuzzy Systems 3(2) : 241~245

[journal] M. Girolami / 2002 / Mercer kernel-based clustering in feature space / IEEE Transactions on Neural Networks 13(3) : 780~784

[journal] M.Filippone / 2008 / A survey of kernel and spectral methods for clustering / Pattern Recognition 41(1) : 176~190

[web] A. Asuncion / UCI Machine Learning Repository / http://www.ics.uci.edu/~ mlearn/ MLRepository

[journal] M. Meila / 2007 / Comparing clusterings-an information based distance / Journal of Multivariate Analysis 98(5) : 873~895
KCI Citation (0)

KJC Korea
Journal Central

Statistics

Tools