Fourth International Conference on Parallel and Distributed Information Systems
DOI: 10.1109/pdis.1996.568665
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A fast distributed algorithm for mining association rules

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Cited by 289 publications
(185 citation statements)
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“…Kantarcioglu and Clifton in [13] use a secure multiparty computation to model the horizontal partitioning of transactions across sites, and present algorithms that incorporate cryptographic techniques to minimize the shared information without incurring much overhead in the mining process. The paper in [14] proposes an efficient distributed algorithm FDM (Fast Distributed Mining of association rules) for mining association rules. Some interesting properties between local and global large itemsets are observed, which leads to an effective technique for the reduction of candidate sets in the discovery of largeitemsets.…”
Section: ) Cryptography-based Techniquesmentioning
confidence: 99%
“…Kantarcioglu and Clifton in [13] use a secure multiparty computation to model the horizontal partitioning of transactions across sites, and present algorithms that incorporate cryptographic techniques to minimize the shared information without incurring much overhead in the mining process. The paper in [14] proposes an efficient distributed algorithm FDM (Fast Distributed Mining of association rules) for mining association rules. Some interesting properties between local and global large itemsets are observed, which leads to an effective technique for the reduction of candidate sets in the discovery of largeitemsets.…”
Section: ) Cryptography-based Techniquesmentioning
confidence: 99%
“…Apriori algorithm was designed to run on databases containing transactions. It is a "bottom up" approach as candidate items are first generated and then the database is scanned to count the support for candidate items exceeding minimum support [23]. The number of items in candidate subsets is increased one at a time with iteration.…”
Section: Generating Interesting Rules From the Frequent Item Sets On mentioning
confidence: 99%
“…Concept of parallel processing [22] and distributed computing [23] are helpful to solve above problems so we can combine both the concepts. We can divide the data then generate processing node and then we can process that distributed data separately in each node after processing we combine the results.…”
Section: Generating Interesting Rules From the Frequent Item Sets On mentioning
confidence: 99%
“…and DHP [66] put forward by Park, etc., and the dividing algorithm PARTITION [67] put forward by Savasere, etc., and the sampling [68] algorithm put forward by Tovionen, etc., and some updating algorithms of the association rule such as FUP, IUA and NEWIUA, and so on. The third one is the algorithm solving the problems of the association rule mining in the distributed environment, such as DMA [71] , FDM, etc. [7278] .…”
Section: The Analysis Of Some Existing Association Rule Mining Algorimentioning
confidence: 99%