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generate and calculate the associated relations and their strengths between documents within a domain. Each document is represented by a bag of words and their weights. We first build domain knowledge background based on the association rules at keyword level, and then we apply those association rules to generate and
A novel text association rule approach FHAR algorithm is presented. To overcome the defect of traditional keywords which does not take into account the semantic relation between keywords, FHAR algorithm in the paper is based on concept vector. The density of semantic field and the weight of meaning are used to
important keywords’ weights and weaken unimportant keywords’ weights. For the knowledge of the categories, we use association rules to improve the precision of text classification and use category priority to represent the relationship between two different categories. Consequently, the experimental results show that our
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