政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111629

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政大機構典藏 > 商學院 > 資訊管理學系 > 會議論文 > Item 140.119/111629

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题名:	MapReduce-Based Frequent Pattern Mining Framework with Multiple Item Support
作者:	Wang, Chen-Shu;Lin, Shiang-Lin;Chang, Jui-Yen 林湘霖
贡献者:	資訊管理學系
关键词:	Association rules;Big data;Database systems;Distributed computer systems;Frequent pattern mining;Growth algorithms;Hadoop MapReduce;Mapreduce frameworks;Multiple items;Parallel and distributed computing;Parallelizations;Real-life applications;Data mining
日期:	2017-02
上传时间:	2017-08-03 14:16:13 (UTC+8)
摘要:	The analysis of big data mining for frequent patterns is become even more problematic. Many efficient itemset mining algorithms to set a multiple support values for each transaction which could seem feasible as real life applications. To solve problem of single support have been discovered in the past. Since, we know that parallel and distributed computing are valid approaches to deal with large datasets. In order to reduce the search space, we using MISFP-growth algorithm without the process of rebuilding and post pruning steps. Accordingly, in this paper we proposed a model to use of MapReduce framework for implement the parallelization under multi-sup values, thereby improving the overall performance of mining frequent patterns and rare items accurately and efficiently. © Springer International Publishing AG 2017.
關聯:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10192 LNAI, 65-74 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017; Kanazawa; Japan; 3 April 2017 到 5 April 2017; 代碼 190369
数据类型:	conference
DOI 連結:	http://dx.doi.org/10.1007/978-3-319-54430-4_7
DOI:	10.1007/978-3-319-54430-4_7
显示于类别:	[資訊管理學系] 會議論文

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