English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90069/105176 (86%)
Visitors : 6462272      Online Users : 156
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


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4756


    Title: Flexible online association rule mining based on the multidimensional pattern relation
    Authors: C. Y. Wang;S. S. Tseng;T. P. Hong
    Contributors: Department of Information Science and Applications
    Keywords: Data mining;Association rule;Incremental mining;Multidimensional mining;Constraint-based mining;Data warehouse
    Date: 2006-06
    Issue Date: 2009-11-30 16:03:26 (UTC+8)
    Publisher: Asia University
    Abstract: Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide ad-hoc, query-driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. We then develop an online mining approach called three-phase online association rule mining (TOARM) based on this proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. The TOARM approach consists of three phases during which final sets of patterns satisfying various mining requests are found. It first selects and integrates related mining information in the multidimensional pattern relation, and then if necessary, re-processes itemsets without sufficient information against the underlying datasets. Some implementation considerations for the algorithm are also stated in detail. Experiments on homogeneous and heterogeneous datasets were made and the results show the effectiveness of the proposed approach.
    Relation: Information Science 176(12):1752-1780
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    0KbUnknown433View/Open
    310904400-4756.doc41KbMicrosoft Word337View/Open


    All items in ASIAIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback