A self-regulating clustering algorithm for identification of minimal cluster configuration
Jiun-Kai Wang, Jeen-Shing Wang · 2005
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.