我們提出了一個新的降低文字特徵維度的方法。我們將文字特徵做階層式的分群，將文字特徵合併成一個新的群聚，用來當作新的特徵，新的特徵可以用來作為分類。一開始每個文字特徵視為獨立的一群，我們計算出所有兩兩不同文字群聚之間的信賴度(confidence)值。然後將有著最高相互信賴度(Mutual confidence)的兩個不相同文字特徵合併起來，成為一個新的群聚。如此不斷重覆合併，直到只剩下一群，或是到達某門檻值為止。利用這樣的方式，使用者可以得到一個階層式的文字分群方法，使用者可以決定想要群聚的數目，從某一層中挑選出來。然後將新的特徵用來作為文件分類。從實驗的結果，可以看出我們的方法相當不錯，優於其他的方法。

We propose a novel feature reduction approach to group words hierarchically into clusters which can then be used as new features for document classification. Initially, each word constitutes a cluster. We calculate the mutual confidence between any two different words. The pair of clusters containing the two words with the highest mutual confidence are combined into a new cluster. This process of merging is iterated until all the mutual confidences between the un-processed pair of words are smaller than a predefined threshold or only one cluster exists. In this way, a hierarchy of word clusters is obtained. The user can decide the clusters, from a certain level, to be used as new features for document classification. Experimental results have shown that our method can perform better than other methods.

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
第一章 簡介 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構 5
第二章 相關研究介紹 6
2.1 文字探勘的定義 6
2.2 文字探勘介紹與流程 7
2.3 群集化（Clustering） 9
2.4 特徵選取（Feature selection） 12
2.5 特徵擷取 （Feature extraction） 14
2.6貝式分類器 （Naïve Bayes Classfier） 17
第三章 我們的研究方法 19
3.1 研究目的和架構 19
3.2 研究方法的詳細介紹與說明 23
3.3 舉例說明我們的方法 27
第四章 實驗分析與結果 33
4.1實驗 Reuters-21587 33
4.2 實驗Cora 36
第五章 結論 40
5.1結論 40
5.2 後續研究與發展 40
參考文獻 42

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