從網際網路搜尋人名相關資訊是個相當方便且普遍的行為，但隨著虛擬網路社會蓬勃的發展，大量的人名相關網頁資訊不斷快速的產生，使用者必須花費相當多的時間來瀏覽過濾所取得的人名資料。這問題主要來自於人名並不具有唯一性，我們可以在網際網路上可查詢到許多同名同姓的人，當然使用者所關注的對象實體也包含在其中。目前許多搜尋引擎都嘗試著去提供名詞實體的對應分類，但針對人名所提供的分類目前較少而且分類的效果也有待改進。因此我們嘗試去對網際網路上所搜尋到包含人名的網頁進行以實體為基礎的群聚分類，其結果可簡化使用搜尋人名資料所花的時間，快速的從大量網頁中取得公開的人名資料,進而獲得搜尋對象的相關資料或是取得連絡方式。
利用特定樣板規則或傳統的文件向量化，來計算不同頁面間相似度的方法是較為普遍的，但這類的方式在真實的應用上存有一些限制。本篇論文提出一個以基於網際網路中的虛擬社會網路鏈結特性，將群聚的方式透過網頁中的超連結達成，並利用超連結文字進一步將部份不包含鏈結資訊的網頁選取到適當的分群中。這個方法最大的好處是不需要使用者提供額外的資訊,可免除額外關鍵字所需要的經驗與認知。經由實驗證明本論文所提出的方法可以獲得良好的群聚效果。

Searching for personal data about people of interest is one of the most popular types of search activity. However, with the exponential growth of the WWW, users need to spend much time on filtering none interesting web pages. This is because personal names are not unique, many different individuals with same person name are found in search result web pages. Currently, many search engines provide functions for automatic classifying web pages; however, there is less function for personal name classifying. Therefore we try to provide a system which can automatically group web pages referred to different individual. The web pages grouping result will reduce the time consuming of users; they can effectively browse these public web pages referred to target individual.
Many past researches utilized predefined template rule or traditional content VSM model to measure the similarity between different web pages, but these kinds of methods have some limitation in real situation. In this thesis, we propose a link-based method by exploiting social network relation in the WWW, then grouping together searched web pages referred to different individual. Additionally, content-based information adopts collect part of web pages which have fewer link relations. Our proposed method need no additional background knowledge for target personal name, thus user can reduce the confusion on setting additional query keyword. The experiments result show that proposed methods can get good achievement in general.

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