Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.artint.2011.09.003 | Artif. Intell. |
Keywords | Field | DocType |
cluster data,model-based multidimensional,analyzes data,complex data,multiple dimension,categorical data,unidimensional clustering,multidimensional clustering,single latent variable,cluster analysis | Fuzzy clustering,Data mining,CURE data clustering algorithm,Artificial intelligence,Cluster analysis,Single-linkage clustering,k-medians clustering,Clustering high-dimensional data,Data stream clustering,Pattern recognition,Correlation clustering,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
176 | 1 | 0004-3702 |
Citations | PageRank | References |
31 | 1.54 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Chen | 1 | 76 | 7.04 |
| Nevin .L Zhang | 2 | 895 | 97.21 |
| Tengfei Liu | 3 | 92 | 7.09 |
| Leonard K. M. Poon | 4 | 94 | 10.96 |
| Yi Wang | 5 | 64 | 5.86 |