Volume 8 Number 12 (Dec. 2013)
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JCP 2013 Vol.8(12): 3251-3256 ISSN: 1796-203X
doi: 10.4304/jcp.8.12.3251-3256

A Fast and Stable Cluster Labeling Method for Support Vector Clustering

Huina Li
Department of Computer Science and Technology, Xuchang University, Xuchang 461000, China
Abstract—Even though support vector clustering (SVC) is able to handle arbitrary cluster shapes effectively, its popularity is frequently degraded by highly intensive time complexity, poor label performance and even instability for efficiency. To overcome such problems, a fast and stable cluster labeling (FSCL) method is proposed. Based on stable equilibrium points, the FSCL first finds an appropriate division of support vectors. With a nonlinear sample sequence strategy presented here, the connected components profiled by support vectors (SVs) can be determined in terms of sampling all stable equilibrium point pairs; and the FSCL prefers a density centroid constructed by one subset of SVs, along with a stable equilibrium point, to represent a component while avoiding local optimization. Finally, the remaining data points can be assigned the label of the nearest components with respect to a weighted distance. Time complexity analysis and comparative experiments suggest that the FSCL improves both the efficiency and clustering quality significantly while guaranteeing stability.

Index Terms—support vector clustering, centroid, stable equilibrium point, unsupervised learning method, support vector machine

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Cite: Huina Li, "A Fast and Stable Cluster Labeling Method for Support Vector Clustering," Journal of Computers vol. 8, no. 11, pp. 3251-3256, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Monthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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