基于阴影集的粗糙聚类阈值选择  

Shadowed Sets Based Threshold Selection in Rough Clustering

作　　者：郭晋华[1] 苗夺谦[1] 周杰[1] 

机构地区：[1]同济大学计算机科学与技术专业,上海201804

出　　处：《计算机科学》2011年第10期209-210,227,共3页Computer Science

基　　金：国家自然科学基金项目(60970061,61085056)资助

摘　　要：粗糙聚类思想自提出以来,在软划分聚类方面取得了广泛应用,但其阈值参数常主观确定,未能考虑数据集本身的特性。基于阴影集(Shadowed Sets)的优化理论给出了一种客观的阈值选择方法,并将其应用于粗糙模糊C均值聚类算法。人工数据与UCI数据实验结果表明了所提方法的有效性。Rough set-based clustering has been applied widely in soft clustering since proposed,but the threshhold is oft-en given subjectively,fails to consider the characteristics of the data set itself.This study based on shadowed sets optimization theory gave an objective thereshold selection method,and applied it to rough fuzzy C-Means clustering algorithm.Artificial data and UCI data experimental results show the effectiveness of the proposed method.

关 键 词：阴影集 粗糙聚类 粗糙模糊聚类 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]