An innovative algorithm for mining multilevel association rules

In this paper, we present a new algorithm for mining multilevel association rules which searches for interesting relationship among items in a given data set at multiple levels in an effective way. This algorithm group the items at data warehousing level for each branch of the decision tree and find the association between them which is effective on a large set of data items. It generates a smaller set of rules, with higher quality and lower redundancy in comparison with general approach of multilevel association rules.

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