Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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This paper proposes the MDMA (Multi-dimensional Distributed Mining Association rules) algorithm based on advanced SQL query. The algorithm works on star-style structure networks. It uses CUBE operator in new standard SQL and combines with SQL powerful query function. So there is no need for the proposed algorithm to conduct a great deal of iterations to generate frequent itemsets in the process of mining association rules. Therefore, no matter what the number of the site in the distributed environments and the scale of the local database in the local site is, the algorithm always needs only two times of scanning the database and only three times of network communications to generate all the global frequent itemsets. Consequently, this algorithm has the merits of light network traffic, low time cost, better scalability and more simplicity.