International Journal of Computational Science and Engineering
Extracting incidental and global knowledge through compact pattern trees in distributed environment
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Novel parallel method for mining frequent patterns on multi-core shared memory systems
DISCS-2013 Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems
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Recently, a significant number of parallel and distributed algorithms have been proposed to mine frequent patterns (FP) from large and/or distributed databases. Among them parallelization of the FP-growth algorithms using the FP-tree has been proved to be highly efficient. However, the FP-tree-based techniques suffer from two major limitations such as multiple database scans requirement (i.e., high I/O cost) and high inter-processor communications cost (during the mining phase). Therefore, we propose a novel tree structure, called PP-tree (Parallel Pattern tree) that significantly reduces the I/O cost by capturing the database contents with a single scan and facilitates the efficient FP-growth mining on it with reduced inter-processor communication overhead. Our parallel algorithm works independently at each local site and locally generates global frequent patterns which are merged at the final stage. The experimental results reflect that parallel and distributed FP mining with PP-tree outperforms other state-of-the-art algorithms.