An Enhanced Scaling Apriori for Association Rule Mining Efficiency

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Executive Summary

This paper proposes an improved Apriori algorithm to minimize the number of candidate sets while generating association rules by evaluating quantitative information associated with each item that occurs in a transaction, which was usually, discarded as traditional association rules focus just on qualitative correlations. The proposed approach reduces not only the number of item sets generated but also the overall execution time of the algorithm. Any valued attribute will be treated as quantitative and will be used to derive the quantitative association rules which usually increases the rules' information content. Transaction reduction is achieved by discarding the transactions that does not contain any frequent item set in subsequent scans which in turn reduces overall execution time.

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