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2016 Technical Reports

Distributed and Parallel High Utility Sequential Pattern Mining

Morteza Zihayat, Zane Zhenhua Hu, Aijun An and Yonggang Hu

Technical Report EECS-2016-03

York University

April 12 2016


The problem of mining high utility sequential patterns(HUSP) has been studied recently. Existing solutions aremostly memory-based, which assume that data can fit intothe main memory of a computer. However, with advent of bigdata, such an assumption does not hold any longer. In thispaper, we propose a new framework for mining HUSPs in bigdata. A distributed and parallel algorithm called BigHUSP isproposed to discover HUSPs efficiently. At its heart, BigHUSPuses multiple MapReduce-like steps to process data in parallel.We also propose a number of pruning strategies tominimize search space in a distributed environment, and thusdecrease computational and communication costs, while stillmaintaining correctness. Our experiments with real life andlarge synthetic datasets validate the effectiveness of BigHUSPfor mining HUSPs from large sequence datasets.

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