计算机工程与科学2018，Vol.40Issue(1)：184-190,7.DOI:10.3969/j.issn.1007-130X.2018.01.027
基于局部密度自适应度量的粗糙K-means聚类算法
Rough K-means clustering based on local density adaptive measure
摘要
Abstract
By introducing the idea of lower and upper approximations,rough K-means has become a powerful algorithm for clustering analysis with overlapping clusters.Its derivative algorithms such as rough fuzzy K-means and fuzzy rough K-means describe the uncertain objects located in the boundaries in detail,thus improving the clustering effect.However,these algorithms do not fully consider the influence of the factors,such as the distance between the data centers of the clusters and the average center and the density of the data distributed in the neighborhood,on the clustering accuracy.Aiming at this problem,a local density adaptive measure method is proposed to describe the spatial characteristics of data objects in a cluster.A rough K-means clustering algorithm based on local density adaptive measure is given.Comparative experimental results of real world data from UCI demonstrate the validity of the proposed algorithm.关键词
粗糙聚类/K-means/局部密度度量/粗糙集Key words
rough clustering/K-means/local density measure/rough sets分类
信息技术与安全科学引用本文复制引用
马福民,逯瑞强,张腾飞..基于局部密度自适应度量的粗糙K-means聚类算法[J].计算机工程与科学,2018,40(1):184-190,7.基金项目
国家自然科学基金(61403184,61105082) （61403184,61105082）
江苏省高校自然科学研究重大项目(17KJA120001) （17KJA120001）
江苏省“青蓝工程”基金(QL2016) （QL2016）
南京邮电大学科研项目(NY215149) （NY215149）
江苏高校优势学科建设工程资助项目(PAPD) （PAPD）
