Mining Interest Set_- Weighted Association Rules Based on Algorithm Apriori
Ying An
Abstract
Ying An
Abstract
Mining association rules,some interesting associations or correlations between items among large quantity of data can be found out and they have many wide applications in some fields.Now,lots of algorithms have been proposed for finding the association rules.Most of these algorithms treat each item as uniformity.However,in real applications,users are more inclined to items they are most interested in or feel most important about.So,in this paper we proposed a new algorithm based on the Interest-set and the weight of item.The Interested item is proposed by user who concerns himself with it and then the relative item is to be found from database.We offer each item a different weight value so that it can represent the importance of each individual item from database.In this way,we can get very valuable rules that algorithm Apriori can't.
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Mining association rules,some interesting associations or correlations between items among large quantity of data can be found out and they have many wide applications in some fields.Now,lots of algorithms have been proposed for finding the association rules.Most of these algorithms treat each item as uniformity.However,in real applications,users are more inclined to items they are most interested in or feel most important about.So,in this paper we proposed a new algorithm based on the Interest-set and the weight of item.The Interested item is proposed by user who concerns himself with it and then the relative item is to be found from database.We offer each item a different weight value so that it can represent the importance of each individual item from database.In this way,we can get very valuable rules that algorithm Apriori can't.
Key concepts: Apriori algorithm, Association rule learning, Computer science, Data mining, Set (abstract data type), A priori and a posteriori, Association (psychology), Value (mathematics)