A self-regulating clustering algorithm for identification of minimal cluster configuration

Jiun Kai Wang, Jeen Shing Wang

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

1 Citation (Scopus)

Abstract

This paper presents a self-regulating clustering algorithm (SRCA) that is capable of identifying the cluster configuration without a priori knowledge regarding the given data set. The proposed SRCA integrates growing, merging, and splitting mechanisms into a systematic framework to identify the minimal cluster configuration. A novel idea of cluster boundary estimation has been proposed to effectively perform the three mechanisms. A virtual cluster spread coupled with a regulating vector enables the proposed SRCA to reveal the compact cluster configuration which may close to the true one. Computer simulations have been conducted to demonstrate the effectiveness of the proposed SRCA in terms of a minimal error of cluster estimation.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages1427-1432
Number of pages6
DOIs
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period04-07-2504-07-29

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Wang, J. K., & Wang, J. S. (2004). A self-regulating clustering algorithm for identification of minimal cluster configuration. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (pp. 1427-1432). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2). https://doi.org/10.1109/IJCNN.2004.1380160