An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
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
Communication-efficient distributed mining of association rules
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Hash based parallel algorithms for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Introduction: Recent Developments in Parallel and Distributed Data Mining
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
Computer
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Parallel Association Rule Mining without Candidacy Generation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors
Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors
A High-Performance Distributed Algorithm for Mining Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
| Hi-index | 0.00 |
Association Rule Mining (ARM) is an active data mining research area. However, most ARM algorithms cater to a centralized environment where no external communication is required. Distributed Association Rule Mining (DARM) algorithms aim to generate rules from different datasets spread over various geographical sites; hence, they require external communications throughout the entire processor. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. DARM algorithms must reduce communication costs. In this paper, a new solution is proposed to reduce the size of message exchanges. Our solution also reduces the size of average transactions and datasets that leads to reduction of scan time, which is very effective in increasing the performance of the proposed algorithm. Our performance study shows that this solution has a better performance over the direct application of a typical sequential algorithm.