摘要
针对邻域粗糙集模型受邻域参数影响大、刻画样本信息时不够精细等问题,提出了一种基于最大联盟理论的粗糙集模型。在标准化邻域信息系统后,引入最大联盟集来描述邻域颗粒信息,使得邻域粗糙集模型对信息的划分更加精细,从而显著降低了边界域的不确定性。将该模型与三支聚类相结合,设计了一种基于最大联盟粗糙集的三支聚类算法。在6个UCI公共数据集上进行对比实验,结果表明,所提算法相较于对比算法具有更好的聚类质量,在处理边界域样本时具有更高的比较正确率。
In order to address the limitations of the neighborhood rough set model caused by neighborhood parameters and inadequate sample information representation,this paper proposed a rough set model based on the maximum coalition theory.By normalizing the neighborhood information system and utilizing the maximum coalition set to describe neighborhood granular information,the proposed model enabled finer information division and significant reduction of uncertainty in the boundary region.Combining the model with three-way clustering,this paper designed a three-way clustering algorithm based on maximum coalition rough set.Comparative experiments on six UCI public data sets show that the proposed algorithm has better clustering quality than the comparison algorithms,and has higher comparison accuracy when dealing with boundary region samples.
作者
陈之琪
万仁霞
岳晓冬
陈瑞典
Chen Zhiqi;Wan Renxia;Yue Xiaodong;Chen Ruidian(College of Mathematics&Information Science,North Minzu University,Yinchuan 750021,China;School of Computer Engineering&Science,Shanghai University,Shanghai 200444,China;Hongyang Institute for Big Data in Health,Fuzhou 350002,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第8期2292-2300,共9页
Application Research of Computers
基金
国家自然科学基金资助项目(62066001)
宁夏自然科学基金资助项目(2021AAC03203)
宁夏科技领军人才项目(2022GKLRLX08)
北方民族大学研究生创新项目(YCX22087)。
关键词
标准化邻域信息系统
最大联盟集
领域粗糙集
边界域
三支聚类
比较正确率
standardized neighborhood information system
maximum coalition set
neighborhood rough sets
boundary region
three-way clustering
comparison accuracy
