近年來，資料探勘相關的研究領域越趨熱絡，除了關聯規則的探勘外，也有學者致力於探討時間因子的關聯規則，大致可區分為消費者購買行為分析、網頁瀏灠分析，以及時間趨勢分析等。而在探討消費者購買行為的順序性上，多藉著候選序列的產生及驗証，以漸進的過程來產生常見序列型樣，然而驗証的過程必須重複掃瞄資料庫，也因此造成系統沈重的負擔而導致效率不佳。本研究提出以“常見型樣相鄰矩陣”來記錄序列的顧客群，在已知常見型樣的前提下，只需再掃瞄資料庫一次即可，而且不需產生候選序列，可以改進常見序列型樣探勘的效率。
然而型樣的存在通常與發生的環境有高度的關聯性，因此必須從很多不同的角度去審視該環境的因素。例如考慮顧客購買商品時的順序關係時，可能還必須把地點、時間、天氣、顧客類別等等客觀因素納入考量，如此探勘所得的序列型樣更能夠符合實際的情況。
在本篇論文中，我們將以常見型樣相鄰矩陣記錄事件的方法為序列探勘演算法，並以約略集合理論(Rough Set Theory)為多維度分析的基礎，只需掃瞄一次資料庫用以對約略集合(Rough Set)建立索引後，結合序列型樣與多維度型樣加以運算，即可求得多維度序列型樣。

Data Mining has become one of the fast growing areas of research in recent years. Besides association rules mining, researchers endeavor to develop mining methods with time factor considered. Popular research topics include customers buying patterns analysis, Internet surfing sequence analysis, trend analysis, and so on. When probing the customers buying sequential patterns, most developed mining methods require repeated database scans to generate candidate patterns, which are then checked to find frequent sequential patterns. It therefore deteriorates the performances of these methods. This paper presents a Frequent Pattern Adjacent Matrix (FPAM) to record intermediate length-2 patterns. After finding the frequent patterns, it only needs one more round of database scan to find all the sequential patterns by taking advantages of FPAM. Without generating unnecessary patterns, the proposed method is an efficient method for mining frequent sequential patterns from databases.
However, the existence of patterns is often related to the circumstances or conditions. A circumstance has to be considered in different views. For example, when a customer buys a product, not only the priority of purchasing, but variables such as region, time, climate and customer category should be also taken into account. A more applicable sequential pattern to the real situation can therefore be mined.
In this paper, we embedded FPAM as the algorithm for sequential patterns mining. Furthermore, we applied Rough Set Theory for multi-dimensional analysis. After a construction of Rough Set index structure, sequential and multi-dimensional patterns are combined to obtain multi-dimensional sequential patterns. With this kind of approach, when given frequent patterns, we can enhance the efficiency of data mining by rescanning the database only once.

中文摘要 I
Abstract II
誌 謝 IV
目 錄 V
圖 目 錄 VIII
表 目 錄 IX
第一章　緒論 1
1.1 資料探勘簡介 2
1.2 問題描述 6
1.3 研究動機 8
1.4 研究目的 10
1.5 論文架構 10
第二章　相關研究 11
2.1 關聯法則的探勘方法 11
2.1.1 Apriori 演算法 13
2.1.2 DHP 演算法 15
2.1.3 Partition 演算法 15
2.1.4 FP-tree 演算法 16
2.1.5 FPL 演算法 17
2.2 順序性資料的探勘方法 20
2.2.1序列型樣探勘 21
2.2.1.1 Apriori-based 演算法 22
2.2.1.2 PSP演算法 25
2.2.1.3 FreeSpan演算法 25
2.2.1.4 PrefixSpan演算法 27
2.2.1.5 SPADE演算法 29
2.2.2 時間序列資料庫探勘 31
2.2.3 網頁存取紀錄探勘 31
2.3 多維度循序型樣的探勘方法 31
第三章　利用常見型樣相鄰矩陣來探勘資料庫序列型樣 33
3.1 主要探勘步驟 33
3.2 演算法及範例說明 37
3.2.1建置常見型樣相鄰矩陣 37
3.2.2 常見相鄰型樣矩陣(FPAM)的探勘過程 41
3.3 實驗結果與討論 44
第四章、多維度序列型樣探勘 48
4.1 主要步驟與架構 53
4.2 詳細步驟說明及範例 55
4.3 實驗結果與討論 59
第五章　結論 62

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