Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Parallel data mining for association rules on shared-memory multi-processors
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Maximal Frequent Itemsets
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A Super-Programming Approach for Mining Association Rules in Parallel on PC Clusters
IEEE Transactions on Parallel and Distributed Systems
Mining frequent itemsets with partial enumeration
Proceedings of the 44th annual Southeast regional conference
Estimation of execution time of data-intensive out-of-core processes
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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In this paper, we propose an algorithm to partition both the search space and the database for the parallel mining of frequent closed itemsets in large databases. The partitioning of the search space is based on splitting the power set lattice of the total item set to two sub-lattices. Conditional databases axe used to partition the large database. The combination of the search space and database partitioning allows parallel processors to mine the frequent closed itemsets independently and thus minimizes the interprocessor communication and synchronization. The partitioning also ensures the load balance among the parallel processors.