ACM Transactions on Database Systems (TODS)
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 database perspective on knowledge discovery
Communications of the ACM
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Methods for Batch Processing of Data Mining Queries
Proceedings of the Baltic Conference, BalticDB&IS 2002 - Volume 1
Simultaneous optimization of complex mining tasks with a knowledgeable cache
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Three strategies for concurrent processing of frequent itemset queries using FP-growth
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
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Frequent itemset mining can be regarded as advanced database querying where a user specifies the dataset to be mined and constraints to be satisfied by the discovered itemsets. One of the research directions influenced by the above observation is the processing of sets of frequent itemset queries operating on overlapping datasets. Several methods of solving this problem have been proposed, all of them assuming selective access to the partitions of data determined by the overlapping of queries, and tested so far only on flat files. In this paper we theoretically and experimentally analyze the influence of data access paths available in database systems on the methods of frequent itemset query set processing, which is crucial from the point of view of their possible applications.