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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/94450

    Title: MOGA-based multi-level genetic-fuzzy mining techniques
    Other Titles: 多目標為基礎的階層式遺傳模糊探勘技術
    Authors: 何吉軒;He, Ji-Syuan
    Contributors: 淡江大學資訊工程學系碩士班
    陳俊豪;Chen, Chun-Hao
    Keywords: 資料探勘;模糊集合;多目標遺傳演算法;分類階層;分群技術;隸屬函數;利潤模糊商品集;模糊關聯規則;data mining;Fuzzy set;multi-objective genetic algorithm;taxonomy;clustering technique;membership function;utility fuzzy itemset;fuzzy association rule
    Date: 2013
    Issue Date: 2014-01-23 14:39:35 (UTC+8)
    Abstract: 現實世界的交易資料通常包含購買數量,故許多因應此類型交易資料的模糊探勘方法被提出並用來挖掘模糊關聯規則。因為隸屬函數對最終探勘結果有重大影響,所以許多遺傳模糊探勘方法進一步被提出用來同時探勘隸屬函數與模糊關聯規則。然而,大部分的方法都專注於單一階層探勘並且只考慮一個目標函數。有鑑於此,本論文提出兩個方法來探勘柏拉圖集合(隸屬函數)並挖掘多階層模糊關聯規則,分別為多目標多階層遺傳模糊探勘方法(MOMLGFM)與兩階段多目標遺傳模糊探勘方法(TMOGFM)。
    Transactions in real-world applications usually consist of quantitative values. Some fuzzy data mining approaches have thus been proposed for deriving linguistic rules from this kind of transactions. Since membership functions may have a critical influence on final mining results, several genetic-fuzzy mining approaches have then been proposed as well for mining appropriate membership functions and fuzzy association rules at the same time. Most of them, however, focus on single-level concept and consider only one objective function. In view of this, this thesis proposes two approaches for mining the Pareto set (a set of non-dominated membership functions) and multi-level fuzzy association rules, namely a Multi-Objective Multi-Level Genetic-Fuzzy Mining Algorithm (MOMLGFM) and a Two-Stage Multi-Objective Fuzzy Mining Algorithm (TMOGFM).
    In the first algorithm (MOMLGFM), it 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 in different concept levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated by these two objective functions. After the MOGA process terminates, various sets of membership functions could be used for deriving multi-level fuzzy association rules according to decision makers’ preferences.
    However, the derived Pareto set by MOMLGFM may be not easy for users to choose an appropriate one for mining rules. In the second algorithm (TMOGFM), based on MOMLGFM, a two-stage multi-objective fuzzy mining algorithm is proposed for assisting decision makers to choose the proper solution. In the first stage, the MOMLGFM is used to derive a set of non-dominated membership functions (Pareto solutions). Then, in second stage, according to the designed rule-oriented or utility-oriented clustering attributes, the clustering technique is utilized to divide the Pareto solutions into groups and find representative solution of each group. The representative solutions of groups could be employed to mine fuzzy association rules or utility fuzzy itemsets according to the favorites of decision makers.
    Experimental results on simulation datasets and a real dataset also show the effectiveness of MOMLGFM and TMOGFM. The advantage of MOMLGFM is that it can derive Pareto set (a set of membership functions) and multi-level fuzzy association rules, simultaneously. The advantage of TMOGFM is that it can not only mine the Pareto set, but also use the representative solutions of groups to acquire multi-level fuzzy association rules and utility fuzzy itemsets.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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