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2004
IEEE

Efficient Data Structures and Parallel Algorithms for Association Rules Discovery

11 years 9 months ago
Efficient Data Structures and Parallel Algorithms for Association Rules Discovery
Discovering patterns or frequent episodes in transactions is an important problem in data-mining for the purpose of infering deductive rules from them. Because of the huge size of the data to deal with, parallel algorithms have been designed for reducing both the execution time and the number of repeated passes over the database in order to reduce, as much as possible, I/O overheads. In this paper, we introduce new approaches for the implementation of two basic algorithms for association rules discovery (namely Apriori and Eclat). Our approaches combine efficient data structures to code different key information (line indexes, candidates) and we exhibit how to introduce parallelism for processing such data-structures.
Christophe Cérin, Gay Gay, Gaël Le Mah
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where ENC
Authors Christophe Cérin, Gay Gay, Gaël Le Mahec, Michel Koskas
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