序列型樣探勘是一個從資料序列中找出有用型樣的探勘方式。最常見的例子為網路記錄存取序列，業主通常可藉由這樣的資訊找出使用者在瀏覽網頁共有的瀏覽方式，進而分析內容而採取商用行為。目前存在的在資料串流中Top-K型樣探勘通常是為非序列型樣，因此在我們的架構中，主要是針對Top-K型樣探勘，讓使用者決定合適的候選者型樣。我們提供的方法為在探勘資料串流中不同批的過程中自動調整最小支持度的大小以達到取得想要的候選者型樣，並將候選者型樣以樹狀結構維持，方便取得最後Top-K型樣的結果。實驗結果說明所提出的方式為有效且規模可變的。

Sequential pattern mining is a process of extracting useful patterns in data sequences. A popular example is web log access sequences. Proprietors usually use such information to find the habits of the users by observing the web pages that they often surf to perform commercial activities. Existing works on mining Top-K patterns on data streams are mostly for non-sequential patterns. In our framework, we focus on the topic of Top-K sequential pattern mining, where users can obtain adequate amount of interesting patterns. The proposed method can automatically adjust the minimum support during mining each batch in the data stream and obtain candidate patterns. Then candidate patterns are maintained by a tree structure for extracting Top-K sequential patterns. Empirical results show that the proposed method is efficient and scalable.

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