Model-based multidimensional clustering of categorical data

被引：71

作者：

Chen, Tao ^{[2
]}

Zhang, Nevin L. ^{[1
]}

Liu, Tengfei ^{[1
]}

Poon, Kin Man ^{[1
]}

Wang, Yi ^{[3
]}

机构：

[1] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China

[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China

[3] Natl Univ Singapore, Dept Comp Sci, Singapore 117417, Singapore

来源：

ARTIFICIAL INTELLIGENCE | 2012年 / 176卷 / 01期

关键词：

Model-based clustering; Categorical data; Multidimensional clustering; Latent tree models; SELECTION; NETWORKS;

D O I：

10.1016/j.artint.2011.09.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified. When one analyzes data using such a model, one is looking for one way to cluster data that is jointly defined by all the attributes. In other words, one performs unidimensional clustering. This is not always appropriate. For complex data with many attributes, it is more reasonable to consider multidimensional clustering, i.e., to partition data along multiple dimensions. In this paper, we present a method for performing multidimensional clustering on categorical data and show its superiority over unidimensional clustering. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：2246 / 2269

页数：24

共 43 条

[1]

NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION [J].