With the advances of technology, data take the form of data streams rather than stored data sets in many recent applications. The research issues on analyzing data streams therefore have received considerable attentions in recent years. Among the issues, finding clusters over data streams is particularly important because it is widely used in many applications such as behavior analyses. This thesis studies an application where multiple clustering tasks with different parameters over data streams are simultaneously asked. In this environment, the approaches proposed in existing literatures for finding clusters are not working effectively or efficiently. Therefore, in this thesis we develop a novel clustering algorithm for efficiently processing multiple clustering tasks with different parameters.
The proposed algorithm is based on the hierarchical clustering method, which progressively merges clusters and therefore multiple clustering tasks can be efficiently processed. Through extensive experiments with synthetic data, we demonstrate the efficiency of using the proposed approach as solutions for finding clusters over data streams.

隨著科技的進步，許多新發展出來的應用環境處理的資料都是以串流的型式出現，而非傳統資庫所處理的資料集合。這一類型資料分析的研究議題在近幾年間愈來愈受到矚目。這些議題中，在資料串流上分群由於被使用在許多應用環境中，例如行為分析，而顯得特別地重要。本篇論文的研究主要在於資料串流上同時被要求多重分群任務的處理。在這樣的環境之中，我們指出現有文獻所使用的分群方法有其效率或效能上的不足之處。因此在這篇論文中，我們發展一種新的分群演算法來有效率地同時處理多重分群的任務。
這個演算法是基於階層式分群的方法，也就是逐步合併較相似的群集，來達到有效率地處理多重分群任務之目的。透過模擬群集所產生資料所做的實驗表現出，使用這個演算法可以有效率地執行在資料串流上的分群任務。

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