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

Jiun Kai Wang, Jeen Shing Wang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2004 IEEE International Joint Conference on Neural Networks - Proceedings
頁面1427-1432
頁數6
DOIs
出版狀態Published - 2004 十二月 1
事件2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
持續時間: 2004 七月 252004 七月 29

出版系列

名字IEEE International Conference on Neural Networks - Conference Proceedings
2
ISSN(列印)1098-7576

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
國家Hungary
城市Budapest
期間04-07-2504-07-29

All Science Journal Classification (ASJC) codes

  • Software

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  • 引用此

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