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研究生： 張簡雅文
Ya-Wen Changchien
論文名稱： 挖掘數值資料之關聯分類規則
Mining Associative Classification Rules from Numerical Data
指導教授： 陳彥良
Yen-Liang Chen
口試委員:
學位類別： 博士
Doctor
系所名稱： 管理學院 - 資訊管理學系
Department of Information Management
畢業學年度： 98
語文別： 英文
論文頁數： 113
中文關鍵詞： 數值資料 、資料探勘 、基因演算法 、關聯分類規則
外文關鍵詞： Data Mining, Genetic Algorithm, Associative Classification, Numerical Data
相關次數： 點閱：11  下載：0 
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    • 關聯式分類是一種資料探勘方法，以關聯規則建構出分類系統。過去研究指出關聯式分類相較於傳統分類方法(如C4.5及ILP)，有較高的分類準確率，然而關聯式分類存在無法處理數值資料以及表達數值資料間關係之缺點。傳統分類方法中的歸納邏輯規劃 (ILP)具有易於關係表達以及對於問題表示與問題特定限制上較具彈性等優點。零容錯率、無法有效處理數值資料以及關係中的參數過多會影響處理效率是納邏輯規劃方法的缺點。本研究首先提出一個多層生物特徵結構的基因演算法(PGA)，改善歸納邏輯規劃系統的缺點。此結構可以表示數值資料間的關係，將之應用於關聯式分類規則編碼並建構出一個關聯式分類系統，以期兼具表達數值資料關係及高分類準確率之優點。實驗結果顯示本研究提出之方法(GA-ACR)具有高預測分類準確率，且優於根據資料分佈決定分類類別之資料分佈法。

    Associative classification, one of data mining techniques, is a classification system based on associative classification rules. Although associative classification is more accurate than traditional classification approaches, such as C4.5 and ILP, it cannot handle numerical data and its relations. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. Inductive logic programming (ILP), one of traditional classification approaches, has great capability of relations representation, and flexibility for problem representation and problem-specific constraints. However, it is not suitable for noisy environment and has weak facilities for processing numerical data, including unsatisfactory learning time with a large number of arguments in the relations. A phenotypic genetic algorithm(PGA) with multi-level phonotypic encoding structure is proposed to solve the problems in the ILP system. This structure has great capability of relations representation between numerical data and is used for relations encoding between numerical data in associative classification rules mining. The experiment results show that the proposed approach(GA-ACR) has high prediction accuracy and is highly competitive when compared with the data distribution method.

    Abstract I 中文摘要 II 誌謝 III Contents IV List of Tables VI List of Figures VII Chapter 1 Introduction 1 1.1 Background 1 1.2. Organization of this Dissertation 4 Chapter 2 Related Works 5 2.1. Data Mining 5 2.2. Inductive Logic Programming (ILP) 7 2.3. Genetic Algorithm (GA) 11 2.3.1. Encoding Structure of GA 13 2.3.2. Multi-level Structure of GA 15 2.4. Associative Classification Rules 18 2.4.1. CBA 20 2.4.2. CMAR 22 2.4.3. CPAR 23 2.4.4. Summary 25 Chapter 3 Phenotypic Encoding Structure of GA 26 3.1. Problem Definition 27 3.2. PGA Algorithm 29 3.2.1. Multi-Level Encoding 31 3.2.2. Fitness Evaluation 34 3.2.3. Crossover 35 3.2.4. Mutation 38 3.3. Experiments 41 3.3.1. Experiment 1: Discover Top-k Rules 41 3.3.2. Experiment 2: Sensitivities of Different Settings 45 3.3.3. Summary 48 Chapter 4 Mining Associative Classification Rules from Numerical Data 49 4.1. Problem Definition 49 4.2. A Phenotypic Encoding Genetic Algorithm for Classification Rule Mining 54 4.2.1 A Phenotype Encoding Genetic Algorithm 55 4.2.2. Classify a New Tuple into a Class 61 Chapter 5 Experiments 63 5.1 Experiment Environment 63 5.2 Experiment 1: Predictive Accuracy 65 5.3 Experiment 2: Sensitivity Test 68 5.3.1 Sensitivity Test for Associative Classification 68 5.3.1 Sensitivity Test for Genetic Algorithm 80 5.4 Summary 101 Chapter 6 Conclusions 102 6.1 Implications for Academic Researches 103 6.2 Implications for Business Practitioners 103 6.3 Future Works 104 References 105

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