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<p><b>Abstract</b>—We discuss the problem of online mining of association rules in a large database of sales transactions. The online mining is performed by preprocessing the data effectively in order to make it suitable for repeated online queries. We store the preprocessed data in such a way that online processing may be done by applying a graph theoretic search algorithm whose complexity is proportional to the size of the output. The result is an online algorithm which is independent of the size of the transactional data and the size of the preprocessed data. The algorithm is almost instantaneous in the size of the output. The algorithm also supports techniques for quickly discovering association rules from large itemsets. The algorithm is capable of finding rules with specific items in the antecedent or consequent. These association rules are presented in a compact form, eliminating redundancy. The use of nonredundant association rules helps significantly in the reduction of irrelevant noise in the data mining process.</p>
OLAP, association rules, data mining, knowledge discovery.
Philip S. Yu, Charu C. Aggarwal, "A New Approach to Online Generation of Association Rules", IEEE Transactions on Knowledge & Data Engineering, vol. 13, no. , pp. 527-540, July/August 2001, doi:10.1109/69.940730
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