期刊
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
卷 54, 期 1, 页码 196-215出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2012.08.001
关键词
Model-based clustering; Facet determination; Variable selection; Latent tree models; Gaussian mixture models
资金
- National Basic Research Program of China (aka the 973 Program) [2011CB505101]
- HKUST Fok Ying Tung Graduate School
Variable selection is an important problem for cluster analysis of high-dimensional data. It is also a difficult one. The difficulty originates not only from the lack of class information but also the fact that high-dimensional data are often multifaceted and can be meaningfully clustered in multiple ways. In such a case the effort to find one subset of attributes that presumably gives the best clustering may be misguided. It makes more sense to identify various facets of a data set (each being based on a subset of attributes), cluster the data along each one, and present the results to the domain experts for appraisal and selection. In this paper, we propose a generalization of the Gaussian mixture models and demonstrate its ability to automatically identify natural facets of data and cluster data along each of those facets simultaneously. We present empirical results to show that facet determination usually leads to better clustering results than variable selection. (C) 2012 Elsevier Inc. All rights reserved.
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