English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56733/90513 (63%)
造访人次 : 12070715      在线人数 : 40
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100103

    题名: Mining high coherent association rules with consideration of support measure
    作者: Chen, Chun-Hao;Lan, Guo-Cheng;Hong, Tzung-Pei;Lin, Yui-Kai
    贡献者: 淡江大學資訊工程學系
    关键词: Data mining;Association rules;Propositional logic;Coherent rules;Highly coherent rules
    日期: 2013-11-15
    上传时间: 2015-01-28 11:07:34 (UTC+8)
    出版者: United Kingdom: Elsevier Science & Technology
    摘要: Data mining has been studied for a long time. Its goal is to help market managers find relationships among items from large databases and thus increase sales volume. Association-rule mining is one of the well known and commonly used techniques for this purpose. The Apriori algorithm is an important method for such a task. Based on the Apriori algorithm, lots of mining approaches have been proposed for diverse applications. Many of these data mining approaches focus on positive association rules such as “if milk is bought, then cookies are bought”. Such rules may, however, be misleading since there may be customers that buy milk and not buy cookies. This paper thus takes the properties of propositional logic into consideration and proposes an algorithm for mining highly coherent rules. The derived association rules are expected to be more meanful and reliable for business. Experiments on two datasets are also made to show the performance of the proposed approach.
    關聯: Expert Systems with Applications 40(16), pp.6531-6537
    DOI: 10.1016/j.eswa.2013.06.002
    显示于类别:[資訊工程學系暨研究所] 期刊論文


    档案 大小格式浏览次数



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈