近年來，資料串流探勘已成為熱門的研究議題，並常被用來解決日益龐大的交易資料。然而，傳統的探勘方式，大多僅用單一模式(model)尋找交易頻繁的樣式，卻容易忽略了實際存在於交易活動中的交易行為模式。譬如，冰品在白天比熱食更為暢銷，將使熱食的交易行為不易被發現並容易被忽略。本研究使用動態串流樹(Dynamic Data Stream Tree)探勘多元關聯法則。使用動態多個循環模式找尋交易的行為樣式，遠比採用單一模式演算法更具多樣化、更準確，且更符合實際存在的交易模式。

Data stream mining is a hot research topic in recent years ,which can sovle the increasingly extensive transactions data. However, the traditional mining methods merely use the single model to find out these frequent trading patterns, which may ignore the practial trading activities in trading patterns easily. For instance, if the ices are sold much better than the hot foods in the daytime, the trading patterns of the hot foods may not be find out and it may be ignored esaily. The study uses dynamic data stream tree and mining multiple association rules. Using the multiple dynamic cycle models to look for the numerous transactions patterns would be more various, accurate and correspondent with the practical transaction patterns than the single model algorithm.

中文摘要i
ABSTRACTii
誌謝iii
目錄iv
表目錄vi
圖目錄vii
一、緒論1
1.1研究背景1
1.2研究動機及目的2
1.3論文架構與內容3
二、文獻探討4
2.1資料串流探勘4
2.2視窗模式4
2.2.1標界模式4
2.2.2傾斜式視窗模式4
2.2.3滑動視窗模式4
2.3相關演算法5
2.3.1精確演算法5
2.3.2近似演算法6
2.4關聯法則6
2.4.1Apriori演算法 7
2.4.2Fp-tree演算法 9
三、動態串流樹探勘演算法12
3.1介紹12
3.2DDST演算法12
3.2.1建立樹狀結構12
3.2.2新生模型樹17
3.2.3合併模型樹20
四、實驗結果與效能分析23
4.1實驗環境與評估方式23
4.2實驗資料集23
4.3效能評估24
五、結論與未來研究方向28

中文部份
陳柏宏(2007)。針對時空性交易資料提供動態視窗關聯法則探勘之研究，國立台南大學數位學習科技研究所碩士論文，未出版。
英文部分
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