English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 89686/119522 (75%)
Visitors : 23955017      Online Users : 560
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/66912
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/66912


    Title: Mining Frequent Itemsets over Distributed Data Streams by Continuously Maintaining a Global Synopsis
    Authors: 陳良弼
    Chen, Arbee L. P.
    Wang, En Tzu
    Contributors: 資科系
    Keywords: Distributed data streams;Data mining;Frequent itemset;Continuous distributed model;Hash-based approach
    Date: 2011.06
    Issue Date: 2014-06-25 16:11:51 (UTC+8)
    Abstract: Mining frequent itemsets over data streams has attracted much research attention in recent years. In the past, we had developed a hash-based approach for mining frequent itemsets over a single data stream. In this paper, we extend that approach to mine global frequent itemsets from a collection of data streams distributed at distinct remote sites. To speed up the mining process, we make the first attempt to address a new problem on continuously maintaining a global synopsis for the union of all the distributed streams. The mining results therefore can be yielded on demand by directly processing the maintained global synopsis. Instead of collecting and processing all the data in a central server, which may waste the computation resources of remote sites, distributed computations over the data streams are performed. A distributed computation framework is proposed in this paper, including two communication strategies and one merging operation. These communication strategies are designed according to an accuracy guarantee of the mining results, determining when and what the remote sites should transmit to the central server (named coordinator). On the other hand, the merging operation is exploited to merge the information received from the remote sites into the global synopsis maintained at the coordinator. By the strategies and operation, the goal of continuously maintaining the global synopsis can be achieved. Rooted in the continuously maintained global synopsis, we propose a mining algorithm for finding global frequent itemsets. Moreover, the correctness guarantees of the communication strategies and merging operation, and the accuracy guarantee analysis of the mining algorithm are provided. Finally, a series of experiments on synthetic datasets and a real dataset are performed to show the effectiveness and efficiency of the distributed computation framework.
    Relation: Data Mining and Knowledge Discovery, 23(2), 252-299
    Source URI: http://dx.doi.org/10.1007/s10618-010-0204-8
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1007/s10618-010-0204-8
    DOI: 10.1007/s10618-010-0204-8
    Appears in Collections:[資訊科學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    252-299.pdf3223KbAdobe PDF738View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    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。
    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。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback