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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15888
Title: Parallel progressive sequential pattern (PPSP) mining
Authors: Singh A.
Gupta M.
Misra M.
Chakrabarti S.
Saha H.N.
Published in: Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018
Abstract: Sequential pattern mining is one of the most important fields in data mining. The progressive sequential pattern mining problem explores the effect of deleting old data from the sequences in the database as well as adding new data to the sequences when sequential patterns are generated and hence generates the most accurate results in data mining applications. With the increasing amount of data, traditional algorithms running on uniprocessors have scalability troubles. We present a novel algorithm to optimize and scale the progressive mining of sequential patterns (PISA) problem on GPUs and achieve a speedup of more than 10× by efficiently utilizing coalesced memory accesses and SIMD execution on GPUs. © 2018 IEEE.
Citation: Proceedings of 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018, (2018), 294- 300
URI: https://doi.org/10.1109/CCWC.2018.8301747
http://repository.iitr.ac.in/handle/123456789/15888
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: CUDA
data mining
GPU
parallel computing
ps tree
ISBN: 9781538646496
Author Scopus IDs: 55487577400
57203756628
56223635000
Author Affiliations: Singh, A., Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, United States
Gupta, M., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Misra, M., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Appears in Collections:Conference Publications [CS]

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