Parallel data mining for association rules on shared-memory multi-processors
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Advances in frequent itemset mining implementations: report on FIMI'03
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A trie-based APRIORI implementation for mining frequent item sequences
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
A Parallel Apriori Algorithm for Frequent Itemsets Mining
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
Optimization of frequent itemset mining on multiple-core processor
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A New Parallel Algorithm for the Frequent Itemset Mining Problem
ISPDC '08 Proceedings of the 2008 International Symposium on Parallel and Distributed Computing
Frequent itemset mining on graphics processors
Proceedings of the Fifth International Workshop on Data Management on New Hardware
Parallel FP-growth on PC cluster
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
GPApriori: GPU-Accelerated Frequent Itemset Mining
CLUSTER '11 Proceedings of the 2011 IEEE International Conference on Cluster Computing
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In this paper we describe a new parallel Frequent Itemset Mining algorithm called "Frontier Expansion." This implementation is optimized to achieve high performance on a heterogeneous platform consisting of a shared memory multiprocessor and multiple Graphics Processing Unit (GPU) coprocessors. Frontier Expansion is an improved data-parallel algorithm derived from the Equivalent Class Clustering (Eclat) method, in which a partial breadth-first search is utilized to exploit maximum parallelism while being constrained by the available memory capacity. In our approach, the vertical transaction lists are represented using a "bitset" representation and operated using wide bitwise operations across multiple threads on a GPU. We evaluate our approach using four NVIDIA Tesla GPUs and observed a 6---30脳 speedup relative to state-of-the-art sequential Eclat and FPGrowth implementations executed on a multicore CPU.