Using market basket analysis to integrate and motivate topics in discrete structures
Proceedings of the 37th SIGCSE technical symposium on Computer science education
Mining traces of large scale systems
ICA3PP'05 Proceedings of the 6th international conference on Algorithms and Architectures for Parallel Processing
An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure
Journal of Medical Systems
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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.