Support vector clustering with a novel cluster validity method

Jen Chieh Chiang, Jeen Shing Wang

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

This paper presents a novel cluster validity method for the support vector clustering (SVC) algorithm to identify an optimal cluster configuration of a given data set. The SVC algorithm is a kernel-based clustering approach that groups a data set into clusters with irregular shapes. Without a priori knowledge of the data sets, a validity measure based on a ratio of cluster compactness to separation with outlier detection has been developed to automatically determine suitable parameters of the kernel functions and soft-margin constants as well. A novel validity measure has been developed to find optimal cluster configurations through an effective parameter searching algorithm. Computer simulations have been conducted on benchmark data sets to demonstrate the effectiveness of the proposed cluster validity method.

原文English
主出版物標題2006 IEEE International Conference on Systems, Man and Cybernetics
發行者Institute of Electrical and Electronics Engineers Inc.
頁面3715-3720
頁數6
ISBN(列印)1424401003, 9781424401000
DOIs
出版狀態Published - 2006 一月 1
事件2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan
持續時間: 2006 十月 82006 十月 11

出版系列

名字Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
5
ISSN(列印)1062-922X

Other

Other2006 IEEE International Conference on Systems, Man and Cybernetics
國家/地區Taiwan
城市Taipei
期間06-10-0806-10-11

All Science Journal Classification (ASJC) codes

  • 工程 (全部)

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