Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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This dissertation proposes a new algorithm of distributed mining association rules using the improved Apriori algorithm, based on analyses and introduction of the basic concepts and algorithms of mining association rules and mining association rules in distributed databases. Using improved Apriori algorithm to directly produce all of local frequent itemset in each crunode, rather than iteratively selecting candidate itemset. Then gather all of local multifarious itemset to broadcast to the general node, producing the global frequent itemset of association rules. In the process, the data is no longer saved with the affair ID as the key word. We take the item ID as the new key word. The performance of the improved Apriori algorithm has been improved through cutting down the store space. While the general node gathers all of local frequent itemset to select the global frequent itemset, it needs only a broadcast probably, needing three broadcasts worst. This raised the efficiency of the new algorithm of Association Rules in Distributed Database System.