為了讓使用者能更便利地瀏覽，許多的研究在討論如何有效率的分類搜尋引擎回傳的資訊。在此篇論文中，我們提出一個新的分群演算法，能將查詢到的搜尋結果分類為階層式的主題。我們把這個演算法命名為 GFIC。 我們利用 $kSSL$ 與 $F_ eta$ measure 的衡量標準來證明我們提出的方法是準確且有效率的。除此之外，我們更進一步的提供一個方法，能增進系統在使用者介面呈現類別時的效果。我們利用一個階層式的點擊模型來觀察使用者瀏覽的行為，透過模型找出在所有分類的網頁中較為熱門的類別。接著根據點擊模型所取得的資訊進一步在使用者介面上調整這些類別擺的位置，讓使用者能快速地看到感興趣的類別。 我們在網路上找到數百名經常瀏覽網頁的自願者，並搜集了他們在系統內瀏覽點擊的歷史資訊。我們用這些搜集的資訊來訓練點擊模型，並用一些點擊模型的衡量標準做效能實驗。我們提供訓練後模型的效能實驗數據及有趣的發現。

Classifying web search results into categories facilitates users' browsing through Web search. For this purpose, many approaches which classify search results efficiently have been proposed. In this paper, we propose a new search result clustering algorithm, named GFIC, to build a topic hierarchy for the search results in response to a query. We use some measures such as $kSSL$ and $F_ eta$ measure for an empirical comparison of the state-of-the-art algorithms, and the results shows that our proposed method is effective and efficient. Besides the method of clustering search result, we also proposed a method to improve the cluster layout of the user interface.
We design a hierarchical click model to analyze click log, and to get hot topics. Therefore, we can adjust the positions of clusters on the user interface, and the users would easily find their interested clusters. Finally, we get a dataset recorded the click log of several hundereds of volunteers which often browse web pages. We use the dataset to train our click model, and then provide some experimental results and some interesting findings.

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