由於網路上儲存著大量的文件，在搜尋時很容易會得到許多重覆的搜尋結果。本論文的出發動機是，為了減少使用者在搜尋時過濾大量重覆資訊的時間。本研究利用醫學資訊系統裡的病患個人資料進行搜尋，減少了使用不精準的關鍵字造成的問題，接著針對搜尋完後，重覆的醫學文章進行分群，讓病患及其家屬能更快的找到與病患疾病相關的資訊。
本研究提出一個少見的分群方法，利用簡單的特徵字擷取方法，將文章轉換為特徵向量，再用主成份分析的方法將維度過高的特徵向量降維度。在計算完全部文章的相似度值後，使用EM algorithm分類成相同與不同兩類，最後再利用Neyman–Pearson Test的假說檢定方法進行相同文章的分群。
實驗結果我們與傳統的K-means分群演算法進行比較，在常用的Precision、Recall與F measure的比較指標上都比K-means的分群結果更好。

Because there are huge data on the web, it will get many duplicate and near-duplicate search results when we search on the web. The motivation of this thesis is that reduce the time of filtering the huge duplicate and near-duplicate information when user search.
In this thesis, we propose a novel clustering method to solve near-duplicate problem. Our method transforms each document to a feature vector, where the weights are terms frequency of each corresponding words. For reducing the dimension of these feature vectors, we used principle component analysis to transform these vectors to another space. After PCA, we used cosine similarity to compute the similarity of each document. And then, we used EM algorithm and Neyman-Pearson hypothesis test to cluster the duplicate documents.
We compared out results with K-means method results. The experiments show that our method is outperformer than K-means method.

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