关键词: 文档聚类, 关联关系, 词条同现, 文档相似度, 潜在语义

Abstract: For the existing vector space model to omit making insufficient semantic relationships between terms in documents representation, this paper proposes a novel document vector representation approach based association relationship. In terms of generalized vector space model, it captures the frequent co-occurrence semantic relations between terms, and then analyzes the correlation between related terms based on association rules, digging out the potential relevance of semantic relationships between terms in the document. It represents documents with linear weighting co-occurrence semantic relations with association semantic. Experimental results show that, compared with the BOW model and GVSM model, the clustering results using association rules document vector represented are more accurate.

Key words: document clustering, association, terms co-occurrence, document similarity, latent semantic