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Adaptive Interval Configuration to Enhance Dynamic Approach for Mining Association Rules
Pages: 1325-1333
Year: Issue:  1

Keyword:  association rulesdata miningdynamic processadaptive algorithm;
Abstract: Most proposed algorithms for mining association rules follow the conventional le vel-wise approach. The dynamic candidate generation idea introduced in the dyna mic itemset counting (DIC) a lgorithm broke away from the level-wise limitation which could find the large i t emsets using fewer passes over the database than level-wise algorithms. However , the dynamic approach is very sensitive to the data distribution of the database and it requires a proper interval size. In this paper an optimization technique named adaptive interval configuration (AIC) has been developed to enhance the d y namic approach. The AIC optimization has the following two functions. The first is that a homogeneous distribution of large itemsets over intervals can be achie ved so that less unnecessary candidates could be generated and less database sca nning passes are guaranteed. The second is that the near optimal interval size c ould be determined adaptively to produce the best response time. We also develop ed a candidate pruning technique named virtual partition pruning to reduce the s ize-2 candidate set and incorporated it into the AIC optimization. Based on the optimization technique, we proposed the efficient AIC algorithm for mining asso c iation rules. The algorithms of AIC, DIC and the classic Apriori were implemente d on a Sun Ultra Enterprise 4000 for performance comparison. The results show th at the AIC performed much better than both DIC and Apriori, and showed a strong robustness.
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