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	PFP: Parallel FP-growth for query recommendation
	Li, Haoyuan ; Wang, Yi ; Zhang, Dong ; Zhang, Ming ; Chang, Edward Y.
	2008
英文摘要	Frequent itemset mining (FIM) is a useful tool for discovering frequently co-occurrent items. Since its inception, a number of significant FIM algorithms have been developed to speed up mining performance. Unfortunately, when the dataset size is huge, both the memory use and computational cost can still be prohibitively expensive. In this work, we propose to parallelize the FP-Growth algorithm (we call our parallel algorithm PFP) on distributed machines. PFP partitions computation in such a way that each machine executes an independent group of mining tasks. Such partitioning eliminates computational dependencies between machines, and thereby communication between them. Through empirical study on a large dataset of 802, 939Web pages and 1, 021, 107 tags, we demonstrate that PFP can achieve virtually linear speedup. Besides scalability, the empirical study emonstrates that PFP to be promising for supporting query recommendation for search engines. ? 2008 ACM.; EI; 0
语种	英语
DOI标识	10.1145/1454008.1454027
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/327634]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Li, Haoyuan,Wang, Yi,Zhang, Dong,et al. PFP: Parallel FP-growth for query recommendation. 2008-01-01.

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