An Improved Clustering Method with Cluster Density Independence

유병현; 김완우; Heo Gyeongyong

KJC Korea
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Journal of The Korea Society of Computer and Information 2021 KCI Impact Factor : 0.62

Korean | English

pISSN : 1598-849X / eISSN : 2383-9945

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An Improved Clustering Method with Cluster Density Independence

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2015, 20(12), pp.15-20

Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

유병현 ¹, 김완우 ¹, Heo Gyeongyong ¹

¹동의대학교

ABSTRACT
열림/닫힘 버튼

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
열림/닫힘 버튼

Clustering, FCM, Cluster density, Density independence

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* 2021년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

[book] J. Bezdek / 1981 / Pattern Recognition with fuzzy Objective Function Algorithms / Springer

[book] Sadaaki Muyamoto / 2015 / Handbook of Computational Intelligence / Springer : 293~248

[journal] Janmenjoy Nayak / 2014 / Fuzzy C-means(FCM) Clustering Algorithm: A Decade Review from 2000 to 2014 / Systems and Technologies 32(2) : 133~179

[journal] Zarita Zainuddin / 2015 / An effective Fuzzy C-Means algorithm based on symmetry similarity approach / Applied Soft Computing 35(10) : 433~448

[report] Basel Abu-Jamous / 2015 / Fuzzy Clustering, Integrative Cluster Analysis in Bioinformatics, Chapter 13

[journal] 허경용 / 2010 / An Extension of Possibilistic Fuzzy C-means using Regularization / 한국컴퓨터정보학회논문지 / 한국컴퓨터정보학회 15(1) : 43~50

[journal] R. N. Dave / 1991 / Characterization and detection of noise in clustering / Pattern Recognition Letters 12(11) : 657~664

[confproc] R. Babuska / 2002 / Improved Covariance Estimation for Gustafson-Kessel Clustering / Proceeding of the 2002 IEEE International Conference on Fuzzy Systems : 1081~1085

[confproc] G. Heo / 2007 / Extension of the Possibilistic Fuzzy C-Means Clustering Algorithm / Proceedings of KFIS Autumn Conference 17(2)

[journal] I. Gath / 1989 / Unsupervised Optimal Fuzzy Clustering / IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7) : 773~791