Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
A fast distributed algorithm for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
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There were some traditional algorithms for mining global frequent itemsets. Most of them adopted Apriori-like algorithm frameworks. This resulted a lot of candidate itemsets, frequent database scans and heavy communication traffic. To solve these problems, this paper proposes a fast algorithm for mining global frequent itemsets, namely the FMGFI algorithm. It can easily get the global frequency for any itemsets from the local FP-tree and require far less communication traffic by the searching strategies of top-down and bottom-up. It effectively reduces existing problems of most algorithms for mining global frequent itemsets. Theoretical analysis and experimental results suggest that the FMGFI algorithm is fast and effective