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 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
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Many association rule algorithms operate in a parallel environment where the database is divided up among a number of processors, a procedure which is usually carried out indiscriminately. The nature of the database partitioning can affect both the number of candidate sets produced and the workload at each processor. This paper demonstrates that Principal Component Analysis can be used successfully to help arrange the records of a database among processors so that efficient load balancing is enabled and candidate set duplication minimised.