Elements of information theory
Elements of information theory
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 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
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th 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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th 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
Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors
Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors
A Super-Programming Approach for Mining Association Rules in Parallel on PC Clusters
IEEE Transactions on Parallel and Distributed Systems
Distributed Mining of Maximal Frequent Itemsets on a Data Grid System
The Journal of Supercomputing
Parallel mining of association rules from text databases
The Journal of Supercomputing
Efficient mining of maximal frequent itemsets from databases on a cluster of workstations
Knowledge and Information Systems
A load-balanced distributed parallel mining algorithm
Expert Systems with Applications: An International Journal
Static load balancing of parallel mining of frequent itemsets using reservoir sampling
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
A multi-agent data mining system for cartel detection in Brazilian government procurement
Expert Systems with Applications: An International Journal
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To mine association rules efficiently, we have developed a new parallel mining algorithm FPM on a distributed share-nothing parallel system in which data are partitioned across the processors. FPM is an enhancement of the FDM algorithm, which we previously proposed for distributed mining of association rules. FPM requires fewer rounds of message exchanges than FDM and, hence, has a better response time in a parallel environment. The algorithm has been experimentally found to outperform CD, a representative parallel algorithm for the same goal. The efficiency of FPM is attributed to the incorporation of two powerful candidate sets pruning techniques: distributed and global prunings. The two techniques are sensitive to two data distribution characteristics, data skewness, and workload balance. Metrics based on entropy are proposed for these two characteristics. The prunings are very effective when both the skewness and balance are high. In order to increase the efficiency of FPM, we have developed methods to partition a database so that the resulting partitions have high balance and skewness. Experiments have shown empirically that our partitioning algorithms can achieve these aims very well, in particular, the results are consistently better than a random partitioning. Moreover, the partitioning algorithms incur little overhead. So, using our partitioning algorithms and FPM together, we can mine association rules from a database efficiently.