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系統識別號 U0026-0812200914153389
論文名稱(中文) 遺傳模糊資料探勘技術之研究
論文名稱(英文) A Study on Genetic-Fuzzy Data Mining Techniques
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 96
學期 2
出版年 97
研究生(中文) 陳俊豪
研究生(英文) Chun-Hao Chen
學號 p7893127
學位類別 博士
語文別 英文
論文頁數 127頁
口試委員 口試委員-李強
口試委員-謝孫源
指導教授-洪宗貝
指導教授-曾新穆
召集委員-金陽和
口試委員-林文揚
口試委員-陳銘憲
中文關鍵字 關聯規則  分群  多目標遺傳演算法  資料探勘  模糊集合  遺傳演算法  隸屬函數 
英文關鍵字 membership function  fuzzy set  genetic algorithm  multi-objective genetic algorithm  data mining  clustering  association rule 
學科別分類
中文摘要 近年來資料探勘技術被廣泛的運用於資料分析。其中因為交易資料可能包含數值、語意和不確定型態的資料,模糊理論因而被應用至此類型的資料分析,且許多探勘演算法亦被提出來挖掘模糊關聯規則。然而,在這些方法中皆假設隸屬函數是事前已知的。所以,如何自動找出適當的隸屬函數和知識挖掘便為重要的環節。
在本論文,我們採用遺傳演算法自動找適合的出隸屬函數,之後利用所得到的隸屬函數進行模糊關聯規則探勘。首先,根據模糊探勘問題的種類和處理商品的方法,我們將遺傳模糊探勘問題分為四大類。模糊探勘問題的種類包括單一支持度模糊探勘問題和多重支持度模糊探勘問題,而處理商品的方式分為整合處理商品和個別處理商品兩種方式。
在以往的研究中,許多遺傳模糊探勘方法都是使用整合處理商品方式針對單一支持度的模糊探勘問題。然而,這些方法在演化的過程是耗時的。故在本論文,我們針對此類型問題提出ㄧ個可以減少演化時間的改良演算法。所提的方法首先利用k-means分群技術將染色體分成k群。在同ㄧ群的每條染色體進而利用所在群的代表染色體的單一大項目集數目和其本身的隸屬函數的合適度值(suitability value)去計算適合度值(fitness value)。如此ㄧ來,演化時間便可以因為減少計算單一大項目集的次數而大幅度的降低。
上述方法是在單一支持度探勘問題下所提出的演算法。而在實際應用上,不同的商品應該有不同的評估標準(即最小支持度)以顯它們的重要性。因此,我們接著使用整合處理商品方式針對多重支持度模糊探勘問題提出ㄧ新穎的演算法。所提的方法首先將所有相似的商品分在同ㄧ群。換句話說,同ㄧ群中的商品即代表有相似的特性,藉此產生較佳之初始母體。而每條染色體的適合度值則是由其需求滿足值(requirement satisfaction)與隸屬函數的合適度(suitability)來評估。因此,所提的方法有兩個優點,第一點為所提的方法可以自動得出每一個商品可接受的最小支持度與隸屬函數,進而用於模糊關聯規則探勘。第二點為透過分群技術,所提的方法可以提供ㄧ較佳的初始母體。上述所提的兩種演算法也可以簡易的延伸至個別處理商品方式上。
此外,在實際應用上不同的評估標準亦是需要被考慮的。可以考慮不同評估標準的權衡關係的多目標演化演算法便非常適合用於解決這樣的問題。相較於遺傳演算法搜尋單一最佳解,多目標遺傳演算法搜尋一個解集合給使用者參考運用,此集合通稱為柏拉圖最佳解(Pareto-Optimal Surface)。故在本論文的最後一部份,我們提出一演算法企圖找出介於兩個評估標準下的柏拉圖最佳解。此兩個評估標準分別為單一大項目集的總數(total number of large 1-itemsets)和隸屬函數的合適度值(suitability)。使用者因此可以選擇恰當的隸屬函數進行模糊關聯規則的探勘。最後,我們進行實驗顯示所提的方法的有效性。從實驗結果,我們可以發現所提的方法中的確可以考慮隸屬函數的合適度程度與所挖掘的知識量。
英文摘要 Data mining techniques have been widely used for data analysis in recent years. Since transactions may consist of quantitative, linguistic and uncertain data, the fuzzy-set theory is applied to these kinds of transactions and many mining approaches are then extended for deriving fuzzy association rules. Membership functions, however, are usually assumed to be known in advance in most of these approaches. How to automatically derive appropriate membership functions and mined knowledge thus presents a challenge.
In this dissertation, the genetic algorithms are adopted to derive a set of appropriate membership functions, which is then used to find fuzzy association rules. We divide the genetic-fuzzy mining problems into four kinds according to the types of fuzzy mining problems and the ways of processing items. The types of fuzzy mining problems include single-minimum-support fuzzy-mining and multiple-minimum-support fuzzy-mining. The ways of processing items include processing all the items together (integrated) and processing them individually (divide-and-conquer).
In the past, several genetic-fuzzy data mining approaches have been proposed for the integrated single-minimum-support problem. They are, however, usually time-consuming on the evaluation process. In this dissertation, we thus first propose an improved approach to speed up of the evaluation time for this type of problem. The proposed approach divides chromosomes into k groups by using the k-means clustering technique. All the chromosomes in a cluster then use the number of large 1-itemsets derived from the representative chromosome in the cluster and their own suitability of membership functions to calculate the fitness values. The evaluation cost can thus be greatly reduced due to the time-saving in finding 1-itemsets.
The above approach is proposed for the mining problem with single minimum supports. In real applications, different items may have different criteria to judge their importance. We then propose a novel approach for the integrated multiple-minimum-support problem. The proposed approach first gathers similar items into groups. All the items in the same group are considered to have similar characteristics and are assigned similar values for initializing a better population. Each chromosome is evaluated by the criteria of requirement satisfaction and suitability of membership functions to estimate its fitness value. The proposed algorithm has two main advantages. The first one is that the proposed approach can derive an acceptable minimum support value and membership functions of each item for fuzzy association-rule mining. The second one is that the proposed approach can get a better initial population, including an appropriate number of linguistic terms, minimum support values, and membership functions of items by using the clustering technique. The above two approaches can also be easily extended to individual processing ways.
Besides, several criteria may be considered at the same time in a real application. The multi-objective evolutionary algorithms, which are used to find a set of solutions with trade-offs among different criteria, are thus very suitable for solving such a task. A set of solutions, namely Pareto-Optimal Surface, is derived and given to users, instead of only the best one solution obtained by genetic algorithms. In the last part of the dissertation, we thus propose an approach to find the Pareto solutions based on the two objective functions, namely the total number of large 1-itemsets and the suitability of membership functions, for deriving membership functions for mining fuzzy association rules. Users can thus choose appropriate solutions to mine fuzzy association rules. At last, experiments are made to show the effectiveness of the proposed approaches. From the experimental results, we find that the proposed approaches can indeed consider well both the suitability of membership function and the amount of mined knowledge.
論文目次 摘 要 I
ABSTRACT III
ACKNOWLEDGEMENTS V
LIST OF FIGURES IX
LIST OF TABLES XII
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 OVERVIEW OF THE DISSERTATION 4
1.2.1 A Cluster-Based Genetic-Fuzzy Mining Approach for Items with Single Minimum Support 4
1.2.2 A Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports 5
1.2.3 A Multi-Objective Genetic-Fuzzy Mining Approach for Items with Single Minimum Support 7
1.3 ORGANIZATION OF THE DISSERTATION 8
CHAPTER 2 REVIEW OF RELATED WORK 9
2.1 DATA MINING 9
2.2 FUZZY SET 11
2.3 FUZZY DATA MINING 13
2.4 GENETIC ALGORITHMS 16
2.5 GENETIC-FUZZY DATA MINING TECHNIQUES 18
2.5.1 Type I Problem: The Integrated Genetic-Fuzzy Mining Problem for Items with a Single Minimum Support 19
2.5.2 Type II Problem: The Divide-and-Conquer Genetic-Fuzzy Mining Problem for Items with a Single Minimum Support 23
CHAPTER 3 GENETIC-FUZZY DATA-MINING METHOD FOR ITEMS WITH SINGLE MINIMUM SUPPORT 26
3.1 INTRODUCTION 26
3.2 A GA-BASED MINING FRAMEWORK 27
3.3 MINING MEMBERSHIP FUNCTIONS AND ASSOCIATION RULES 29
3.3.1 Chromosome Representation 29
3.3.2 Initial Population 30
3.3.3 Fitness and Selection 30
3.3.4 Clustering Chromosomes 31
3.3.5 Genetic Operators 32
3.4 THE PROPOSED MINING ALGORITHM (ICGFSMS) 33
3.5 AN EXAMPLE 37
3.6 EXPERIMENTAL RESULTS 43
3.6.1 Description of the Experimental Datasets 43
3.6.2 Performance of the Proposed Algorithm 43
3.6.3 Comparison Results of the Previous Approach (IGFSMS) and the Proposed Approach (ICGFSMS) 48
3.6.4 Parameters Setting Evaluation on the Proposed Approach 51
3.6.5 Different Data Distributions Evaluation on the Proposed Approach 56
3.7 SUMMERY 64
CHAPTER 4 GENETIC-FUZZY DATA-MINING METHOD FOR ITEMS WITH MULTIPLE MINIMUM SUPPORTS 65
4.1 INTRODUCTION 65
4.2 A GA-BASED MINING FRAMEWORK 66
4.3 MINING MEMBERSHIP FUNCTIONS AND ASSOCIATION RULES 68
4.3.1 Chromosome Representation 68
4.3.2 Initial Population 70
4.3.3 The Required Number of Large 1-itemsets 76
4.3.4 Fitness and Selection 77
4.3.5 Genetic Operators 79
4.4 THE PROPOSED MINING ALGORITHM 80
4.5 AN EXAMPLE 82
4.6 EXPERIMENTAL RESULTS 85
4.6.1 Description of the Experimental Datasets 86
4.6.2 The Performance of the Proposed Approach 86
4.7 SUMMARY 93
CHAPTER 5 MULTI-OBJECTIVE GENETIC-FUZZY MINING APPROACH FOR ITEMS WITH SINGLE MINIMUM SUPPORT 95
5.1 INTRODUCTION 95
5.2 GA-BASED MULTI-OBJECTIVE OPTIMIZATION PROBLEMS 96
5.3 THE MULTI-OBJECTIVE GENETIC-FUZZY MINING APPROACH 99
5.3.1 Chromosome Representation 99
5.3.2 Initial Population 99
5.3.3 The Two Objective Functions 100
5.3.4 Fitness Assignment 102
5.3.5 Genetic Operators 104
5.4 THE PROPOSED MINING ALGORITHM 105
5.5 AN EXAMPLE 108
5.6 EXPERIMENTAL RESULTS 114
5.6.1 The Evolution of Pareto Fronts by the Proposed Approach 114
5.6.2 The Effects with Minimum Supports and Minimum Confidences 116
5.7 SUMMERY 118
CHAPTER 6 CONCLUSIONS AND FUTURE WORKS 120
BIBLIOGRAPHY 123
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