DETERMINE OPTIMUM NUMBER OF COMPACT OVERLAPPED CLUSTERS USING FRLVQ TECHNIQUE  

作　　者：Xu Wenhuan Huang Qiang Ji Zhen Zhang Jihong 

机构地区：[1]Faculty of Information Engineering, Shenzhen University, Shenzhen 518060, China

出　　处：《Journal of Electronics(China)》2005年第6期676-680,共5页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No.60172065).

摘　　要：A method, named XHJ-method, is proposed in this letter to determine the number of clusters of a data set, which incorporates with the Fuzzy Reinforced Learning Vector Quantization (FRLVQ) technique. The simulation results show that this new method works well for the traditional iris data and an artificial data set, which contains un-equally sized and spaced clusters.A method, named XHJ-method, is proposed in this letter to determine the number of clusters of a data set, which incorporates with the Fuzzy Reinforced Learning Vector Quantization （FRLVQ） technique. The simulation results show that this new method works well for the traditional iris data and an artificial data set, which contains un-equally sized and spaced clusters.

关 键 词：Reinforced learning Vector quantization Clustering analysis 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]