拓展集合差异度高维数据聚类  

Clustering for high dimensional data based on extended set dissimilarity

作　　者：武森[1] 叶俞飞[1] 俞晓莉[1,2] 

机构地区：[1]北京科技大学经济管理学院,北京100083 [2]海信集团海信学院,山东青岛266071

出　　处：《计算机应用研究》2011年第9期3253-3255,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(70771007);中央高校基本科研业务费专项资金资助项目(FRF-TP-10-006B)

摘　　要：提出度量多个集合之间总体差异程度的拓展集合差异度及相关定理,并给出一种新的解决分类属性高维数据聚类问题的CAESD算法。基于拓展集合差异度及拓展集合特征向量,在CABOSFV_C聚类的基础上通过两阶段聚类完成全部聚类过程。采用UCI数据集与K-modes及其改进算法、CABOSFV_C算法进行比较实验,结果表明CAESD算法具有较高的聚类正确率。This paper proposed extended set dissimilarity and related theory to measure the general dissimilarity among sets,and proposed a new algorithm to cluster high dimensional data named as clustering algorithm based on extended set dissimilarity for categorical attributes（CAESD）,which executed two steps clustering process using extended set dissimilarity and extended set feature vector on the basis of CABOSFV_C algorithm.Comparative tests using UCI data sets show that CAESD algorithm has higher clustering accuracy than K-modes algorithm,improved approaches of K-modes and CABOSFV_C algorithm.

关 键 词：高维数据聚类 CABOSFV_C算法 拓展集合差异度 CAESD算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]