資料挖掘的目的在於如何從資料庫中，擷取出感興趣以及具有意義的頻繁項目集，而其中一個子題，時序性資料探勘，由於其廣泛利用使得此議題，所以，近幾年受到許多注意，其主要的考量在於資料庫中分析具有時間性的資料，並且由其中發現出具時間性的項目集以及規律。在日常生活中，普遍存在的各種模糊性現象，包含人類的思維以及決策等，皆存在有模糊的特質，模糊理論強調許多事情的結果，無法符合傳統的非0即1的二元邏輯，而是介於之間。在論文中，我們提出了三個模糊時序性資料探勘的方法，分別去做探討，針對交易資料庫中每筆項目的交易資料，以及他們所發生的時間，去做資料探勘的動作，這三個方法分別處理不同的時間區間的定義。第一個方法是針對每個項目以它所發生的第一筆交易時間至最後時間為評估的時間週期；第二個方法則是根據每個項目實際出版的時間作為評估週期；第三個方法則是找出在某一時間至最後時間裡，皆為頻繁狀態的時間作為評估週期。本論文所提的方法則是在架構在上述三種不同時間週期來找出各自的模糊週期性關聯規則。最後，實驗結果也證實本論文所提的方法確實能有效的針對不同時間週期進行資料挖掘的程序，在出版時序資料庫裡，能找出更多具感興趣的模糊週期規則。

Data mining is an important process of extracting desirable knowledge from existing databases for specific purposes. Nearly all transactions in real-world databases involve items bought, quantities of the items, and the time periods in which they appear. In the past, temporal quantitative mining was proposed to find temporal quantitative rules from a temporal quantitative database. However, the quantitative values of items are not suitable to human reasoning. To deal with this, the fuzzy set theory was applied to the temporal quantitative mining because of its simplicity and similarity to human reasoning. In this thesis, we thus handle the problem of mining fuzzy temporal association rules from a publication database, and propose three algorithms to achieve it. The three algorithms handle different lifespan definitions, respectively. In the first algorithm, the lifespan of an item is evaluated from the time of the first transaction with the item to the end time of the whole database. In the second algorithm, an additional publication table, which includes the publication date of each item in stores, is given, and thus the lifespan of an item is measured by its entire publication period. Finally in the third algorithm, the lifespan of an item is calculated from the end time of the whole database to its earliest time in the database for the item to be a fuzzy temporal frequent item within the duration. In addition, an effective itemset table structure is designed to store and get information about itemsets and can thus speed up the execution efficiency of the mining process. At last, experimental results on two simulation datasets compare the mined fuzzy temporal quantitative itemsets and rules with and without consideration of lifespans of items under different parameter settings.

Chapter 1 Introduction 1
1.1 Background and Motivation 1
Chapter 2 Review of Related Works 3
2.1 Binary Data Mining 3
2.2 The Apriori Algorithm 4
2.3 Fuzzy Data Mining 5
2.4 Fuzzy Temporal Data Mining 6
2.5 Publication Information Table 7
2.6 Membership Functions 9
2.7 Problem Statement and Definitions 12
Chapter 3 Fuzzy Temporal Association Rule Mining Approach 13
3.1 An Example 17
Chapter 4 General Fuzzy Temporal Association Rule Mining 24
4.1 An Example 30
Chapter 5 General Fuzzy Temporal Association Rules with Effect Lifespans 46
5.1 An example 52
Chapter 6 Experiment Results and Discussion 71
Chapter 7 Conclusion and Future Works 77
References 79

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