Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29780
Title: A subspace decision cluster classifier for text classification
Authors: Li, Y
Hung, E
Chung, K 
Keywords: Classification
Subspace decision cluster
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 10, p. 12475-12482 How to cite?
Journal: Expert systems with applications 
Abstract: In this paper, a new classification method (SDCC) for high dimensional text data with multiple classes is proposed. In this method, a subspace decision cluster classification (SDCC) model consists of a set of disjoint subspace decision clusters, each labeled with a dominant class to determine the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a subspace clustering algorithm Entropy Weighting k-Means algorithm. Then, the SDCC model is extracted from the subspace decision cluster tree. Various tests including Anderson-Darling test are used to determine the stopping condition of the tree growing. A series of experiments on real text data sets have been conducted. Their results show that the new classification method (SDCC) outperforms the existing methods like decision tree and SVM. SDCC is particularly suitable for large, high dimensional sparse text data with many classes.
URI: http://hdl.handle.net/10397/29780
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2011.04.029
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