挖掘序列型樣是資料挖掘中一項重要的技術，主要用於找出序列資料庫中的頻繁子序列。一般挖掘序列型樣的演算法大多能正確找出頻繁項目集之間的發生先後順序，但對於其出現前後的間隔時間卻無法加以描述。
　　為了有效解決此一問題，本研究結合階層式分群法與模糊數的概念，提出一個挖掘模糊時間序列型樣的演算法，不僅可以挖掘出序列資料庫中所有頻繁項目及其出現的順序關係，更可將其出現的間隔時間以合理的方式表示。

An important task of sequential patterns mining is to discover frequent sequential patterns in a sequence database.  Conventional sequential patterns only reveal the order of items, information about time intervals between successive by occurred items has not been determined.
  In this paper, we proposed an algorithm called fuzzy time sequential pattern mining (FTSP).  We use the hierarchical clustering technique to cluster the time intervals between successive itemsets, and define a fuzzy number to each time cluster to compute the fuzzy support, and then we have mined the frequent fuzzy time sequential patterns.
Fuzzy time sequential patterns mining, reveals not only the order of items, but also the time intervals between successive items.

目錄 Ⅰ
表目錄 Ⅲ

第1章 緒論 1
　　1.1　研究動機與目的 1
　　1.2　論文架構 2
第2章 資料挖掘與序列型樣 3
　　2.1　資料挖掘 3
　　2.2　序列型樣 12
　　2.3　時間序列型樣 21
第3章 模糊時間序列型樣之研究 24
　　3.1　資料分群法 24
　　3.2  階層式分群法 27
　　3.3  模糊集合論 29
　　3.4  模糊數 30
　　3.5  模糊時間序列型樣 35
第4章 模糊時間序列型樣之實驗 40
　　4.1　模糊時間序列型樣演算法運算步驟 40
　　4.2　實驗結果 50
第5章 結論 51
　　5.1　結論 51
　　5.2　未來研究方向 52
參考文獻 53
附錄—英文論文 55

表目錄

Table 4.1　Sequence database 40
Table 4.2　Itemset and its support 41
Table 4.3　Candidate 2-squences, C2 42
Table 4.4　Frequent 2-squences, L2 43
Table 4.5　Time-clusters of frequent 2-sequence and its frequency 44
Table 4.6　Fuzzy number representation and fsupp of each time cluster 45
Table 4.7　Fuzzy number of frequent 2-sequence, L2 46
Table 4.8　Frequent fuzzy time 2-sequences, L2’ 47
Table 4.9　Candidate frequent fuzzy time 3-sequences, C3’ 48
Table 4.10　Frequent fuzzy time 3-sequences, L3’ 49
Table 4.11　Candidate frequent fuzzy time 4-sequences, C4’ 49

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