ASIA unversity:Item 310904400/4753
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 92958/108462 (86%)
Visitors : 20404042      Online Users : 227
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:

    Title: Online generation of association rules under multidimensional consideration based on negative-border
    Authors: Ching-Yao Wang;S. S. Tseng;Tzung-Pei Hong;Yian-Shu Chu
    Contributors: Department of Information Science and Applications
    Keywords: Apriori algorithm;association rule;data mining;incremental mining;multidimensional mining, negative border
    Date: 2005
    Issue Date: 2009-11-30 16:03:25 (UTC+8)
    Publisher: Asia University
    Abstract: Recently, some researchers have developed incremental and online mining approaches to maintain association rules without having to re-process the entire database whenever the database is updated or user specified thresholds are changed. However, they usually can not flexibly obtain association rules or patterns from portions of data, consider problems with different aspects, or provide online decision support for users. We earlier developed an online mining approach for generation of association rules under multidimensional consideration. The multidimensional online mining approach may, however, get loose upper-bound support of candidate itemsets and thus cause excessive I/O and computation costs. In this paper, we attempt to apply the concept of a negative border to enlarge the mining information in the multidimensional pattern relation to help get tighter upper-bound, and thus reduce the number of candidate itemsets to consider. Based on the extended multidimensional pattern relation, a corresponding online mining approach called Negative-Border Online Mining (NOM) is proposed to efficiently and effectively utilize the information of negative itemset in the negative border. Experiments for heterogeneous datasets are also performed to show the effectiveness of the proposed approach.
    Relation: Journal of Information Science and Engineering 23:233-242
    Appears in Collections:[Department of Applied Informatics and Multimedia] Journal Article

    Files in This Item:

    File Description SizeFormat
    310904400-4753.doc44KbMicrosoft Word403View/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