模糊資料探勘技術能有效地透過將數量資訊轉換為模糊函式的方法，找出資料庫中隱藏的語意關聯規則，但良好的模糊函式是決定模糊資料探勘最終關聯規則品質的重要關鍵，因此過去有許多研究提出使用遺傳演算法訓練並提升模糊函式之質量來有效提升關聯規則之質量。但這類方法仍有執行時間過長的問題，且在模糊函式訓練完成後，對於頻繁項目集的挖掘同樣是一件相當費時的程序。因此在本篇論文中，我們提出一系列以MapReduce為基礎的演算法來加快遺傳模糊探勘的整體速度。本篇論文的貢獻可分為三部分，包括原始資料的預處理、使用遺傳演算法訓練模糊函式以及模糊關聯規則的推導，所有程序都使用MapReduce作分散式處理；資料的預處理除了能將其轉換為MapReduce架構所需之key-value格式外，更進一步將各自物品的數值資訊統整起來，有效的減少多餘的資料庫掃描次數；針對遺傳模糊函式訓練的部分，最耗時的fitness計算將被設計為分散式計算；最後，本研究設計了一個採用分散式FP-growth的方法來提升尋找模糊關聯規則的執行效率。單機與MapReduce版本的效能將會在實驗中比較及討論，其結果顯示本論文所提出的分散式方法能有效的縮短整體模糊探勘的執行時間。

Fuzzy data mining can successfully find out hidden linguistic association rules by transforming quantity information into fuzzy membership values. In the derivation process, good membership functions play a key role in achieving the quality of finial results. In the past, some researches were proposed to train membership functions by genetic algorithms and could indeed improve the quality of found rules. Those kinds of methods were, however, suffered from the long execution time in the training phase. Besides, after appropriate fuzzy membership functions are found, mining out the frequent itemsets from them is also a very time-consuming process as traditional data mining. In this thesis, we thus propose a series of approaches based on the MapReduce architecture to speed up the GA-fuzzy mining process. The contributions can be divided into three parts, including data preprocessing, membership-function training by GA, and fuzzy association-rule derivation. All are performed by MapReduce. For data preprocessing, the proposed approach can not only transform the original data into key-value format to fit the requirement of MapReduce, but also efficiently reduce the redundant database scan by joining the quantities into lists. For membership-function training by GA, the fitness evaluation, which is the most time-costly process, is distributed to shorten the execution time. At last, a distributed fuzzy rule mining approach based on FP-growth is designed to improve the time efficiency of finding fuzzy association rules. The performance between using a single processor and using MapReduce will be compared and discussed from experiments and the results show that our approaches can efficiently reduce the execution time of the whole process.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Table viii
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Contributions 3
1.3 Organization 4
CHAPTER 2 Related Works 6
2.1 Genetic Fuzzy Mining 6
2.2 MapReduce 8
2.3 Parallel FP-Growth 9
CHAPTER 3 Efficient Data Preprocessing for Fuzzy Mining with MapReduce (EDPFM-MR) 12
3.1 Problem Statement and Definitions 13
3.2 Proposed Algorithm, EDPFM-MR 14
3.3 An Example of Using EDPFM-MR 16
CHAPTER 4 Parallel Genetic Fuzzy Membership Function Training with MapReduce (PGFMFT-MR) 23
4.1 Problem Statement and Definitions 25
4. 1. 1 Chromosome Representation 25
4. 1. 2 Initial Population 26
4. 1. 3 Fitness Function 26
4. 1. 4 Genetic Operators 30
4.2 Proposed Algorithm, PGFMFT-MR 31
4.3 An Example of Using PGFMFT-MR 34
CHAPTER 5 Parallel FP-Growth for Fuzzy Mining with MapReduce (PFPGFM-MR) 46
5.1 Problem Statement and Definitions 47
5.2 The Proposed PFPGFM-MR 48
5.3 An Example of Using PFPGFM-MR 51
CHAPTER 6 Experimental Evaluation 62
6.1 Experimental Datasets 62
6.2 Experimental Results of EDPFM-MR 62
6.3 Experimental Results of PGFMFT-MR 65
6.4 Experimental Result of PFPGFM-MR 67
CHAPTER 7 Conclusion 71
References 73

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