Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Parallel Apriori Algorithm for Frequent Itemsets Mining
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
String analysis by sliding positioning strategy
Journal of Computer and System Sciences
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Apriori algorithm is one of the basic algorithms introduced to solve the problem of frequent itemset mining (FIM). Since there is a new generation of affordable computers with parallel processing capability and it is easier to set up computer clusters, we can develop more efficient parallel FIM algorithms for these new systems. This paper investigates the use of trie data structure in parallel execution of Apriori algorithm, the potential problems during implementation, performance comparison of several parallel implementations and in order to increase the efficiency, proposes a new way of message passing for parallel Apriori on a computer cluster with PVM.