政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/127184

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 109948/140897 (78%)
Visitors : 46103116 Online Users : 1186

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 > Item 140.119/127184

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/127184

Title:	MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support
Authors:	張瑞岩王貞淑*
Contributors:	資管博三
Keywords:	big data analytics;Hadoop MapReduce parallel computing;frequent pattern discovery;multiple item support
Date:	2019-05
Issue Date:	2019-10-30 11:14:20 (UTC+8)
Abstract:	In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.
Relation:	Applied Sciences, Vol.9, No.10, pp.2075
Data Type:	article
Appears in Collections:	[資訊管理學系] 期刊論文

Files in This Item:

File	Description	Size	Format
245.pdf		1611Kb	Adobe PDF2	144	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...