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dc.contributor.authorLin, Kawuu W.en_US
dc.contributor.authorChung, Sheng-Haoen_US
dc.contributor.authorHsiao, Chun-Yuanen_US
dc.contributor.authorLin, Chun-Chengen_US
dc.contributor.authorChen, Pei-Lingen_US
dc.date.accessioned2018-08-21T05:54:20Z-
dc.date.available2018-08-21T05:54:20Z-
dc.date.issued2016-11-01en_US
dc.identifier.issn1607-9264en_US
dc.identifier.urihttp://dx.doi.org/10.6138/JIT.2016.17.6.20150603cen_US
dc.identifier.urihttp://hdl.handle.net/11536/145817-
dc.description.abstractIn distributed computing environments, frequent pattern mining by a multi-computing node can greatly improve mining efficiency. However, the drawback of memory limitations may cause interruption in the kernel and computing nodes when recursively building a frequent pattern (FP) tree or an FP-growth algorithm. In this paper, we propose disk-based FP-tree generation and node-based clustering mechanisms to solve the insufficient memory problem. Results from empirical evaluations show that the proposed method delivers excellent scalability.en_US
dc.language.isoen_USen_US
dc.subjectData miningen_US
dc.subjectFrequent pattern miningen_US
dc.subjectClusteringen_US
dc.subjectDistributed computingen_US
dc.titleA Disk-Based Mining Algorithm for Frequent Pattern Discovery from Big Data in Distributed Computing Environmentsen_US
dc.typeArticleen_US
dc.identifier.doi10.6138/JIT.2016.17.6.20150603cen_US
dc.identifier.journalJOURNAL OF INTERNET TECHNOLOGYen_US
dc.citation.volume17en_US
dc.citation.spage1259en_US
dc.citation.epage1268en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000389625000021en_US
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