A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments

Authors:
Kawuu W. Lin;Der-Jiunn Deng
Affiliations:
Department of Computer Science and Information Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 80778, Taiwan.;Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua, Taiwan
Venue:
International Journal of Ad Hoc and Ubiquitous Computing
Year:
2010

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Abstract

Parallel and distributed computing techniques have attracted extensive attentions on the ability to manage and compute the significant amount of data in the past decades. The difficulty of mining large database launched the research of designing parallel and distributed algorithms to solve the problem. In this paper, we propose a novel data mining algorithm, named Cloud-based Association Rule Mining (CARM), abbreviated as CARM, which is able to efficiently utilise the nodes to discover frequent patterns in cloud computing environments with data privacy preserved. Through empirical evaluations on various simulation conditions, the proposed CARM delivers excellent performance in terms of scalability and execution time.