關聯規則探勘常用於找出資料中具有價值的資訊。而在實際的交易資料庫中，物品通常伴隨著購買數量出現。模糊理論因此被應用於此類型的資料探勘，且許多探勘演算法亦被提出來挖掘模糊關聯規則。然而在過往的研究中，大多專注於利用第一型隸屬函數進行探勘，因此在本論文中，我們採用第二型隸屬函數進行資料挖掘。第二型隸屬函數相較於第一型隸屬函數較為一般化，所以能夠處理資料中更多的不確定性。我們首先提出一個利用第二型隸屬函數挖掘模糊關聯規則的方法。數值化的資料首先透過事先給定的第二型隸屬函數轉為第二型模糊區間，我們接著提出一個重心法將該模糊區間降為模糊值，並利用該值進行模糊關聯規則探勘。隸屬函數對於探勘的結果有著重大的影響，又由於多數的模糊關聯規則探勘演算法需要給定隸屬函數，因此我們針對此問題提出一個學習合適第二型隸屬函數的方法。該方法基於遺傳演算法進行學習，首先將隸屬函數編碼為染色體並設計合理的基因運算，藉由遺傳演算法的幫助找出適當的第二型隸屬函數。最後我們對探勘結果的品質做更進一步的調整，提出一個採用兩組式表示法編碼的染色體及針對提高模糊關聯規則信賴度的適應函數。利用上述提出之方法進行的實驗結果顯示對於模糊關聯規則的數量及品質的確得到提升。

Association rule mining is commonly utilized to extract useful information from given data. Since items are usually with quantities in real-world transaction databases, the fuzzy set theory is applied to many mining approaches for deriving fuzzy association rules. In the past, fuzzy mining mainly focused on type-1 membership functions. In this thesis, we attempt to use type-2 membership functions for mining. Type-2 fuzzy sets are generalization of type-1 fuzzy sets and are able to handle more uncertainty than type-1. An interval type-2 fuzzy association rule mining approach is first proposed in this thesis. Rules are mined by predefined interval type-2 membership functions. The quantitative transactions are transformed into fuzzy values according to the corresponding type-2 membership functions. The interval type-2 fuzzy values will be reduced to type-1 values by a centroid type reduction method in order to induce fuzzy association rules. Since membership functions are usually assumed to be known in advance in most of the fuzzy data mining approaches, thus a GA-based type-2 fuzzy association rule mining is proposed to learn appropriate type-2 membership functions. The type-2 membership functions of each item are encoded as a chromosome and appropriate genetic operators are designed to find good solutions. In order to further enhance the quality of mining results, another GA-based representation, the 2-tuple linguistic representation, is also proposed. It adopts a different tuning mechanism and a modified evaluation function for the chromosomes to evolve. Experiments are also made to show the effectiveness of the proposed approaches. From the experimental results, the proposed approaches can mine more rules than using type-1 membership functions, and the qualities of rules are improved as well.

摘要 ii
Abstract iii
Content iv
List of Figures vii
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Organization of the Thesis 4
CHAPTER 2 Review of Related Work 6
2.1 Type-2 Fuzzy Sets 6
2.2 Fuzzy Data Mining 7
2.3 Genetic-Fuzzy Mining 8
CHAPTER 3 Fuzzy Data Mining with Type-2 Membership Functions 10
3.1 Introduction 10
3.2 Proposed Approach 10
3.3 An Example 15
CHAPTER 4 Genetic-Fuzzy Mining with Type-2 Membership Functions 23
4.1 Introduction 23
4.2 A GA-Based Mining Framework 24
4.2.1 Chromosome Representation 25
4.2.2 Initial Population 27
4.2.3 Fitness Function and Selection 27
4.2.4 Genetic Operators 30
4.3 Proposed Approach 30
4.4 An Example 33
CHAPTER 5 Genetic-Fuzzy Mining with Tuning Mechanisms 41
5.1 Introduction 41
5.2 A GA-Based Tuning Framework 42
5.2.1 Chromosome Representation 43
5.2.2 Initial Population 44
5.2.3 Fitness Function and Selection 44
5.2.4 Genetic Operators 47
5.3 Proposed Approach 48
5.4 An Example 50
CHAPTER 6 Experimental Evaluation 59
6.1 Datasets 59
6.2 Experimental Results 60
6.2.1 Evaluations on Proposed T2FM 60
6.2.2 Evaluations on Proposed T2GFM 63
6.2.3 Evaluations on Proposed 2-Tuple T2GFM 66
CHAPTER 7 Conclusions and Future Work 70
References 72

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