结合最近邻与闭模式子空间聚类方法  

Nearest neighbor and closed pattern subspace clustering

作　　者：宋奎勇 王念滨[1] 王红滨[1] 寇香霞 SONG Kuiyong;WANG Nianbin;WANG Hongbin;KOU Xiangxia(College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China;Department of Information Engineering, Hulunbuir Vocational Technical College, Hulunbuir, Inner Mongolia 021000, China)

机构地区：[1]哈尔滨工程大学计算机科学与技术学院,哈尔滨150000 [2]呼伦贝尔职业技术学院信息工程系,内蒙古呼伦贝尔021000

出　　处：《计算机工程与应用》2017年第16期134-137,198,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61272185;No.61502037);黑龙江省自然科学基金(No.F201340);基础科研项目(No.JCKY2016206B001;No.JCKY2014206C002)

摘　　要：针对传统距离度量在高维数据上效果不明显问题,提出一种共享最近邻子空间聚类算法(SNN_SC),按照维把数据集转变为多个最近邻事务数据库,挖掘事务数据库中最大共现对象集,即一维上聚类。在一维聚类集上进一步挖掘闭频繁项集,包含闭频繁项集的维是子空间,闭频繁项集是子空间上聚类。实验对比结果表明,SNN_SC能够更准确定位子空间,并在子空间上产生完整聚类。According to the measurement results in high dimensional data is not obvious problems of the traditional distance,proposes a shared nearest neighbor subspace clustering algorithm(SNN_SC),according to the dimension of the data set into multiple nearest neighbor transaction database mining in transaction database maximum co-occurrence object set,namely dimension clustering.On the one dimensional clustering set,the closed frequent itemsets are further exploited.The dimension of the closed frequent itemsets is a subspace.The experimental results show that SNN_SC can more accurately locate the subspace,and generate a complete clustering in subspace.

关 键 词：高维 共享最近邻 子空间聚类 闭频繁项集 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]