A cluster validity measure with outlier detection for support vector clustering

Jeen Shing Wang, Jen Chiang Chiang

研究成果: Article同行評審

89 引文 斯高帕斯(Scopus)


This paper focuses on the development of an effective cluster validity measure with outlier detection and cluster merging algorithms for support vector clustering (SVC). Since SVC is a kernel-based clustering approach, the parameter of kernel functions and the soft-margin constants in Lagrangian functions play a crucial role in the clustering results. The major contribution of this paper is that our proposed validity measure and algorithms are capable of identifying ideal parameters for SVC to reveal a suitable cluster configuration for a given data set. A validity measure, which is based on a ratio of cluster compactness to separation with outlier detection and a cluster-merging mechanism, has been developed to automatically determine ideal parameters for the kernel functions and soft-margin constants as well. With these parameters, the SVC algorithm is capable of identifying the optimal number of clusters with compact and smooth arbitrary-shaped cluster contours for the given data set and increasing robustness to outliers and noise. Several simulations, including artificial and benchmark data sets, have been conducted to demonstrate the effectiveness of the proposed cluster validity measure for the SVC algorithm.

頁(從 - 到)78-89
期刊IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
出版狀態Published - 2008 2月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 資訊系統
  • 人機介面
  • 電腦科學應用
  • 電氣與電子工程


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