Parallel database systems: the future of high performance database systems
Communications of the ACM
Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parallel mining algorithms for generalized association rules with classification hierarchy
SIGMOD '98 Proceedings of the 1998 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
PDIS '94 Proceedings of the third international conference on on Parallel and distributed information systems
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Dynamic Load Balancing for Parallel Association Rule Mining on Heterogenous PC Cluster Systems
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Effect of Data Skewness in Parallel Mining of Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Performance Analysis for Parallel Generalized Association Rule Mining on a Large Scale PC Cluster
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
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PC cluster is recently regarded as one of the most promising platforms for heavy data intensive applications, such as decision support query processing and data mining. We proposed some new parallel algorithms to mine association rule and generalized association rule with taxonomy and showed that PC cluster can handle large scale mining with them. During development of high performance parallel mining system on PC cluster, we found that heterogeneity is inevitable to take the advantage of rapid progress of PC hardware. However we can not naively apply existing parallel algorithms since they assume homogeneity. We proposed the new dynamic load balancing methods for association rule mining, which works under heterogeneous system. Two strategies, called candidate migration and transaction migration are proposed. Initially first one is invoked. When the load imbalance cannot be resolved with the first method, the second one is employed, which is costly but more effective for strong imbalance. The experimental results confirm that the proposed approach can very effectively balance the workload among heterogeneous PCs.