期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 37, 期 3, 页码 2696-2704出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2009.09.026
关键词
Concept lattice; Formal concept analysis; Fuzzy K-Means clustering; Singular value decomposition
类别
资金
- Dept. of Science and Technology, Govt. of India [SR/S3/EECE/25/2005]
During the design of concept lattices, complexity plays a major role in computing all the concepts from the huge incidence matrix. Hence for reducing the size of the lattice, methods based on matrix decompositions like SVD are available in the literature. However, SVD computation is known to have large time and memory requirements. In this paper, we propose a new method based on Fuzzy K-Means clustering for reducing the size of the concept lattices. We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization. (C) 2009 Elsevier Ltd. All rights reserved.
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