Support vector clustering with a novel cluster validity method

Jen Chieh Chiang, Jeen Shing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Systems, Man and Cybernetics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3715-3720
Number of pages6
ISBN (Print)1424401003, 9781424401000
DOIs
Publication statusPublished - 2006 Jan 1
Event2006 IEEE International Conference on Systems, Man and Cybernetics - Taipei, Taiwan
Duration: 2006 Oct 82006 Oct 11

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume5
ISSN (Print)1062-922X

Other

Other2006 IEEE International Conference on Systems, Man and Cybernetics
CountryTaiwan
CityTaipei
Period06-10-0806-10-11

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Support vector clustering with a novel cluster validity method'. Together they form a unique fingerprint.

Cite this