目前在文本探勘或是主題偵測上的研究上，鮮少建立在微網誌的環境下。而微網誌所提供的搜尋系統往往無法搜尋到有效的資料。在文本探勘當中較常使用到的Fuzzy C-Mean卻往往因為中心點的找尋不同而有不同的結果，在文本探勘上也造成許多問題。

本論文建立一個改良過的搜尋系統，使用者輸入關鍵字之後可以得到分類之後的結果，且了解目前針對關鍵字的主要話題為何。並將系統結合Google應用服務引擎技術，讓大眾可以使用本系統，並在最低硬體成本的消耗下達到最大的效益。另外本論文針對傳統Fuzzy C-Mean演算法做改進，不僅大幅的增加了分群的準確度，也縮短的分群的時間。

In the researches of text mining and topic detection, there are few researches base on micro blog. And the result of search engine which is supported by website seldom finds the useful data. In text mining or data mining, Fuzzy C-Mean is a popular method. The result strongly depends on the center point. And the result shows that different center point have different result. It may cause many problems in text mining and data mining.

In this thesis, we build an on-line micro blog search engine. When user enter the word which he interest in, the system shows the result and classified. The precision, recall and run time is good. And we construct our system on Google App Engine. Let more people use our system. We improve the Fuzzy C-Mean algorithm; enhance the precision of Fuzzy C-Mean.

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