期刊
INFORMATION PROCESSING & MANAGEMENT
卷 49, 期 2, 页码 484-496出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2012.09.003
关键词
Association rule mining; Associative classification; Text categorization; Large-scale dataset
资金
- MKE (The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program [NIPA-2012-(H0301-12-3001)]
Associative classification methods have been recently applied to various categorization tasks due to its simplicity and high accuracy. To improve the coverage for test documents and to raise classification accuracy, some associative classifiers generate a huge number of association rules during the mining step. We present two algorithms to increase the computational efficiency of associative classification: one to store rules very efficiently, and the other to increase the speed of rule matching, using all of the generated rules. Empirical results using three large-scale text collections demonstrate that the proposed algorithms increase the feasibility of applying associative classification to large-scale problems. (C) 2012 Elsevier Ltd. All rights reserved.
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