摘要
首先结合减法聚类和模糊C-均值聚类各自的优点,运用减法聚类自适应地确定模糊C-均值聚类(FCM)的初始聚类数;然后,提出了改进的紧密性函数,以此改进用于确定FCM聚类结构的有效性函数.改进后的紧密性函数将对聚类结果贡献不大的数据予以剔除,使得算法适应能力更强,执行速度更快.实验结果表明,该快速紧密性函数是有效的,而且计算速度更快.
Firstly,the advantages of subtractive clustering and fuzzy C-means clustering（FCM）are combined to automatically determine the initial number of clusters in FCM.Then,an improved close function which is used in the function of validity to determine the cluster structure of FCM is proposed.The improved close function of cluster validity index ignores the data that have a faint impact on the result of clusters and leads to a stronger adaptability and a faster calculating.The experiment results show that the improved close function is effective and faster for computing.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第7期1074-1078,共5页
Control and Decision
基金
山东省自然科学基金项目(ZR2010GM013)
