Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Database Systems Concepts
Efficient Mining of Association Rules in Distributed Databases
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Data Organization and Access for Efficient Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Generalized Stochastic Tree Automata for Multi-relational Data Mining
ICGI '02 Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications
Pattern mining on stars with FP-growth
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Finding patterns in large star schemas at the right aggregation level
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Granular association rule mining through parametric rough sets
BI'12 Proceedings of the 2012 international conference on Brain Informatics
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Available technology for mining data usually applies to centrally stored data (i.e., homogeneous, and in one single repository and schema). The few extensions to mining algorithms for decentralized data have largely been for load balancing. In this paper, we examine mining decentralized data for the task of finding frequent itemsets. In contrast to current techniques where data is first joined to form a single table, we exploit the inter-table foreign key relationships to obtain decentralized algorithms that execute concurrently on the separate tables, and thereafter, merge the results. In particular, for typical warehouse schema designs, our approach adapts standard algorithms, and works efficiently. We provide analyses and empirical validation for important cases to exhibit how our approach performs well. In doing so, we also compare two of our approaches in merging results from individual tables, and thereby, we exhibit certain memory vs I/O trade-offs that are inherent in merging of decentralized partial results.