Model-based multidimensional clustering of categorical data

Tao CHEN, Nevin L. ZHANG, Tengfei LIU, Kin Man POON, Yi WANG

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

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. Copyright © 2011 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)2246-2269
JournalArtificial Intelligence
Volume176
Issue number1
DOIs
Publication statusPublished - Jan 2012

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Cluster analysis
cluster analysis
Categorical
Cluster Analysis
Superiority

Bibliographical note

Chen, T., Zhang, N. L., Liu, T., Poon, K. M., & Wang, Y. (2012). Model-based multidimensional clustering of categorical data. Artificial Intelligence, 176(1), 2246-2269. doi: 10.1016/j.artint.2011.09.003

Keywords

  • Model-based clustering
  • Categorical data
  • Multidimensional clustering
  • Latent tree models