An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient Hardware Data Mining with the Apriori Algorithm on FPGAs
FCCM '05 Proceedings of the 13th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
An Architecture for Efficient Hardware Data Mining using Reconfigurable Computing Systems
FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Hardware-Enhanced Association Rule Mining with Hashing and Pipelining
IEEE Transactions on Knowledge and Data Engineering
Distributed and Shared Memory Algorithm for Parallel Mining of Association Rules
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A Reconfigurable Platform for Frequent Pattern Mining
RECONFIG '08 Proceedings of the 2008 International Conference on Reconfigurable Computing and FPGAs
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Mining frequent itemsets in large databases is a widely used technique in Data Mining. Several sequential and parallel algorithms have been developed, although, when dealing with high data volumes, the execution of those algorithms takes more time and resources than expected. Because of this, finding alternatives to speed up the execution time of those algorithms is an active topic of research. Previous attempts of acceleration using custom architectures have been limited because of the nature of the algorithms that have been conceived sequentially and do not exploit the intrinsic parallelism that the hardware provides. The innovation in this paper is a highly parallel algorithm that utilizes a vertical bit vector (VBV) data layout and its feasibility for making support counting. Our results show that for dense databases a custom architecture for this algorithm can perform faster than the fastest architecture reported in previous works by one order of magnitude.