Fuzzy clustering based on <i>K</i>-nearest-neighbours rule

作     者：Zahid, N Abouelala, O Limouri, M Essaid, A 

作者机构：Univ Mohammed V AGDAL Fac Sci Lab Concept & Syst Rabat Morocco 

出 版 物：《FUZZY SETS AND SYSTEMS》 (模糊集与系)

年 卷 期：2001年第120卷第2期

页      面：239-247页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

主　　题：cluster analysis fuzzy c-means algorithm K-nearest-neighbours decision rule fuzzy sets pattern recognition 

摘      要：In cluster analysis, several algorithms have been made for partitioning a set of objects into c natural clusters. in general, this problem is formulated as being an objective function optimization approach. However, it is known that the function being minimized is nonconvex and hence it may lead to convergence to many local minima, i.e., to different partitions. Thus, the clustering is repeated with different initializations hoping that some runs will lead to the global minimum. Therefore, the performance of these algorithms depends largely on good choice of these initializations. The most widely used algorithm using this function is called fuzzy c-means algorithm (FCMA). Zn this paper a new algorithm is proposed to carry out fuzzy clustering without a priori assumptions on initial guesses. This algorithm is based on two-layer clustering strategy. During the first layer, the K-nearest-neighbours decision rule is used. Then, to achieve an optimal partition, the second layer involves one iteration of FCMA. The performance of the proposed algorithm and that of FCMA have been tested on six data sets. The results obtained show that the new algorithm possesses a number of advantages over the FCMA. (C) 2001 Elsevier Science B.V. All rights reserved.