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Mining for association rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper, the authors present an efficient algorithm for mining association rules that is faster than the previously proposed partition algorithms approximately m times where m is the number of stages in pipeline. The algorithm is also ideally suited for parallelization. Association rule mining is to find out association rules that satisfy the predefined minimum support and confidence from a given database.
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