Clustering Categorical Data Based on Within-Cluster Relative Mean Difference  

作　　者：Jinxia Su Chunjing Su 

机构地区：[1]School of Mathematics and Statistics, Lanzhou University, Lanzhou, China

出　　处：《Open Journal of Statistics》2017年第2期173-181,共9页统计学期刊（英文）

摘　　要：The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple method to find such distinctive features by comparing pooled within-cluster mean relative difference and then partition the data upon such features and give subspace of the subgroups. The applications on zoo data and soybean data illustrate the performance of the proposed method.The clustering on categorical variables has received intensive attention. In dataset with categorical features, some features show the superior performance on clustering procedure. In this paper, we propose a simple method to find such distinctive features by comparing pooled within-cluster mean relative difference and then partition the data upon such features and give subspace of the subgroups. The applications on zoo data and soybean data illustrate the performance of the proposed method.

关 键 词：CLUSTERING CATEGORICAL Variable Distinctive Attribute Pooled Within-Cluster Mean RELATIVE DIFFERENCE Hamming Distance 

分 类 号：R73[医药卫生—肿瘤]