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SIGMOD
1997
ACM

Scalable Parallel Data Mining for Association Rules

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Scalable Parallel Data Mining for Association Rules
One of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The most time consuming operation in this discovery process is the computation of the frequency of the occurrences of interesting subset of items (called candidates) in the database of transactions. To prune the exponentially large space of candidates, most existing algorithms consider only those candidates that have a user defined minimum support. Even with the pruning, the task of finding all association rules requires a lot of computation power and memory. Parallel computers offer a potential solution to the computation requirement of this task, provided efficient and scalable parallel algorithms can be designed. In this paper, we present two new parallel algorithms for mining association rules. The Intelligent Data Distribution algorithm efficiently uses aggregate memory of the parallel computer by employing intelligent...
Eui-Hong Han, George Karypis, Vipin Kumar
Added 07 Aug 2010
Updated 07 Aug 2010
Type Conference
Year 1997
Where SIGMOD
Authors Eui-Hong Han, George Karypis, Vipin Kumar
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