Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Using a Hash-Based Method with Transaction Trimming for Mining 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 trie-based APRIORI implementation for mining frequent item sequences
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Index-BitTableFI: An improved algorithm for mining frequent itemsets
Knowledge-Based Systems
Trie: An alternative data structure for data mining algorithms
Mathematical and Computer Modelling: An International Journal
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Many algorithms have been proposed for the discovery of association rules. The efficiency of these algorithms needs to be improved to handle realworld large datasets. This efficiency can be determined mainly by three factors. The way candidates are generated, the way their supports are counted and the data structure used. Most papers focus on the first and the second factors while few focus on the underlying data structures. In this paper, we present a distributed Multi-Agent based algorithm for mining association rules in distributed environments. The distributed MAS algorithm uses Bit vector data structure that was proved to have better performance in centralized environments. The algorithm is implemented in the context of Multi-Agent systems and complies with global communication standard Foundation for Intelligent Physical Agents (FIPA). The distributed Multi-Agent based algorithm with its new data structure improves implementations reported in the literature that were based on Apriori. The algorithm has better performance over Apriori-like algorithms.