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Feature Extraction of Web Document using Association Word Mining  

고수정 (인하대학교 대학원 전자계산공학과)
최준혁 (김포대학 컴퓨터계열)
이정현 (인하대학교 전자계산공학과)
Publication Information
Journal of KIISE:Databases / v.30, no.4, 2003 , pp. 351-361 More about this Journal
Abstract
The previous studies to extract features for document through word association have the problems of updating profiles periodically, dealing with noun phrases, and calculating the probability for indices. We propose more effective feature extraction method which is using association word mining. The association word mining method, by using Apriori algorithm, represents a feature for document as not single words but association-word-vectors. Association words extracted from document by Apriori algorithm depend on confidence, support, and the number of composed words. This paper proposes an effective method to determine confidence, support, and the number of words composing association words. Since the feature extraction method using association word mining does not use the profile, it need not update the profile, and automatically generates noun phrase by using confidence and support at Apriori algorithm without calculating the probability for index. We apply the proposed method to document classification using Naive Bayes classifier, and compare it with methods of information gain and TFㆍIDF. Besides, we compare the method proposed in this paper with document classification methods using index association and word association based on the model of probability, respectively.
Keywords
feature extraction; association word mining;
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Times Cited By KSCI : 2  (Citation Analysis)
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