A validity-guided support vector clustering algorithm for identification of optimal cluster configuration

Jen Chieh Chiang, Jeen-Shing Wang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

This paper presents a validity-guided support vector clustering (SVC) algorithm for identifying an optimal cluster configuration. Since the SVC is a kernel-based clustering approach, the parameter of kernel functions plays a crucial role in the clustering result. Without a priori knowledge of data sets, a validity measure, based on a ratio of overall cluster compactness to separation, has been developed to automatically determine a suitable parameter of the kernel functions. Using this parameter, the SVC algorithm is capable of identifying the optimal cluster number with compact and smooth arbitrary-shaped cluster boundaries. Computer simulations have been conducted to demonstrate the effectiveness of the proposed validity-guided SVC algorithm.

Original languageEnglish
Pages (from-to)3613-3618
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume4
Publication statusPublished - 2004

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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