Model-based multidimensional clustering of categorical data |
| |
Authors: | Tao Chen Nevin L. Zhang Tengfei Liu Kin Man Poon Yi Wang |
| |
Affiliation: | 1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China;3. Department of Computer Science, National University of Singapore, Singapore 117417, Singapore |
| |
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. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|