English  |  正體中文  |  简体中文  |  Items with full text/Total items : 49433/84396 (59%)
Visitors : 7463704      Online Users : 41
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/87930

    Title: A study on fuzzy coherent data-mining techniques
    Other Titles: 具一致性的模糊資料探勘方法之研究
    Authors: 李艾芳;Li, Ai-Fang
    Contributors: 淡江大學資訊工程學系碩士班
    Keywords: 模糊規則;隸屬函數;一致性規則;領域衍生資料探勘;高一致性效益頻率項目集;Fuzzy rule;membership functions;coherent rule;domain-driven data mining;high coherent utility fuzzy itemsets
    Date: 2012
    Issue Date: 2013-04-13 11:52:48 (UTC+8)
    Abstract: 在真實世界中,交易資料通常包含數值型資料。因此,許多利用模糊理論的方法被提出用來從數值型資料中探勘模糊關聯規則。此外,由於每個商品有它自己的利潤,最近這幾年利潤商品集探勘領域也變得相當受歡迎。然而這些方法的共通問題,第一、這些方法的最小支持度不易設定;第二、所探勘出的規則只揭露常識性的資訊,使得這些規則不具有商業價值。故本論文中,我們提出兩個具有命題邏輯特性的演算法,分別為模糊一致性規則(FCR)及高一致性利潤模糊商品集(HCUFI)去克服上述問題。
    In real-world applications, transactions usually contain quantitative values. Many fuzzy data mining approaches have been proposed for finding fuzzy association rules from the give quantitative transactions. In addition, since each item has its own utility, utility itemset mining has thus become an interesting field in recent years. However, the common problems of those approaches are that: first, an appropriate minimum support is not easy to be set; second, the derived rules usually expose common-sense knowledge which may not interesting in business point of view. In this thesis, we thus propose two algorithms, called Fuzzy Coherent Rules (FCR) mining and High Coherent Utility Fuzzy Itemsets (HCUFI) mining, to overcome the mentioned problems with the properties of propositional logic.
    The first algorithm first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are further collected to generate candidate fuzzy coherent rules. Finally, contingency tables for every candidate fuzzy coherent rules are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. If yes, it is then a fuzzy coherent rule.
    In second algorithm, due to each item has its utility, we first propose a domain-driven fuzzy data-mining framework. According to the framework, we further propose a high coherent utility fuzzy itemsets mining algorithm for increasing patterns’ business merits. It first transforms quantitative transactions into fuzzy sets. Then, utility of each fuzzy itemsets is then calculated according to the given external utility table. If the value is large than or equals to the minimum utility ratio, it is considered as high utility fuzzy itemset (HUFI). Finally, contingency tables are calculated and used for checking those HUFIs satisfy specific four criteria or not. If yes, it is a High Coherent Utility Fuzzy Itemsets (HCUFI).
    Experiments on the foodmart dataset have also been made to show the efficiency of these two proposed approaches. The advantage of first algorithm is that it can derive business interestingness rules with propositional logic without setting minimum support. And, the advantage of second algorithm is that it can derive more actionable knowledge pattern with business interestingness based on the domain-driven fuzzy data-mining framework.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

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