An Efficient Approach for Extraction Positive and Negative Association Rules from Big Data
Abstract
Mining association rules is an significant research area in Knowledge Extraction. Although the negative association rules have notable advantages, but they are less explored in comparaison with the positive association rules. In this paper, we propose a new approach allowing the mining of positive and negative rules. We define an efficient method of support counting, called reduction-access-database. Moreover, all the frequent itemsets can be obtained in a single scan over the whole database. As for the generating of interesting association rules, we introduce a new efficient technique, called reduction-rules-space. Therefore, only half of the candidate rules have to be studied. Some experiments will be conducted into such reference databases to complete our study.
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