淡江大學機構典藏:Item 987654321/98879
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62797/95867 (66%)
造访人次 : 3733608      在线人数 : 330
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98879


    题名: MOGA-based fuzzy data mining with taxonomy
    作者: Chen, Chun-Hao;He, Ji-Syuan;Hong, Tzung-Pei
    贡献者: 淡江大學資訊工程學系
    关键词: Data mining;Fuzzy sets;Fuzzy rules;Multi-objective genetic algorithm;Taxonomy
    日期: 2013-09
    上传时间: 2014-09-24 13:35:44 (UTC+8)
    出版者: Elsevier BV
    摘要: Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from such transactions. Since membership functions may have a critical influence on the final mining results, several genetic-fuzzy mining approaches have been proposed for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on a single level and consider only one objective function. This paper proposes a multi-objective multi-level genetic-fuzzy mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. The algorithm first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. Two objective functions are then considered. The first one is the knowledge amount mined out at different levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated using these two objective functions. After the evolutionary process terminates, various sets of membership functions can be used for deriving multi-level fuzzy association rules according to decision-makers. Experimental results on the simulated and real datasets show the effectiveness of the proposed algorithm.
    關聯: Knowledge-Based Systems 54, pp.53-65
    DOI: 10.1016/j.knosys.2013.09.002
    显示于类别:[資訊工程學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML173检视/开启
    MOGA-based fuzzy data mining with taxonomy.pdf3590KbAdobe PDF1检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

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