NDLI: A fast parallel algorithm for discovering frequent patterns

Content Provider	IEEE Xplore Digital Library
Author	Lin, K.W. Yu-Chin Luo
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Information Engineering, National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. (Lin, K.W.; Yu-Chin Luo)
Abstract	Fast discovery of frequent patterns is the most extensively discussed problem in data mining fields due to its wide applications. As the size of database increases, the computation time and the required memory increase severely. The difficulty of mining large database launched the research of designing parallel and distributed algorithms to solve the problem. Most of the past studies tried to parallelize the computation by dividing the database and distribute the divided database to other nodes for mining. This approach might leak data out and evidently is not suitable to be applied to sensitive domains like health-care. In this paper, we propose a novel data mining algorithm named FD-Mine that is able to efficiently utilize the nodes to discover frequent patterns in cloud computing environments with data privacy preserved. Through empirical evaluations on various simulation conditions, the proposed FD-Mine delivers excellent performance in terms of scalability and execution time.
Starting Page	398
Ending Page	403
File Size	2900437
Page Count	6
File Format	PDF
ISBN	9781424448302
DOI	10.1109/GRC.2009.5255089
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Cloud computing Data privacy Computational modeling Data mining privacy preserved Parallel algorithms Distributed computing association rule mining Concurrent computing Distributed databases cloud computing Distributed algorithms frequent pattern mining
Content Type	Text
Resource Type	Article

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