人類常在接受到許多未知或未清楚定義之事物時，會自然而然的將相似的事物聚為一類。類神經網路一直以來常被用來模擬人腦之運作，其中自我組織圖為最被廣泛使用之模式，其主要用以模擬人類腦中之物以類聚之模式。雖然我們可以將相似的事物歸為一類，但卻不能立即得知該群聚中所蘊藏之內隱知識，須對該群集進行充分的解析才能給予適當的主題名稱作為代表。本文描述如何從一堆文件資料中發掘更多內隱知識之方法。在本研究中，我們發展一適用於文件資料之可調性自我組織圖模型，即主題導向式自我組織圖，來將文件資料進行分群並建立階層結構。本模式與傳統自我組織圖主要的不同是依各群集之主題來進行自我組織圖之橫向擴展與縱向階層建立。如此建立之結構將可以更深入了解該文件集之內涵。我們使用路透社文件集(Reuter-21578)來建構實驗以發掘文件間之內隱知識。本研究實驗顯示我們的方法可以對文字文件發展出符合其內涵之文件結構。

Text document clustering is a basic operation of text processing and is widely applied in data visualization, theme identification, text summarization, hierarchy generation, etc. However, it will be inconvenient for users to find a document after clustering without proper labeling of topics. Moreover, there exist hierarchical relationships between document clusters. In this work, we will propose an adaptive self-organizing map model, namely the topic-oriented self-organizing map (TOSOM), that can adaptively expand the map laterally and hierarchically according to the topics of clusters, rather than the data distributions used in traditional adaptive self-organizing maps such as growing hierarchical self-organizing map (GHSOM). We conducted experiments on the Reuters-21578 dataset and obtained promising result.

目錄
致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究架構 4
第二章 文獻探討 5
2.1 可調整式自我組織圖 5
2.1.1 自我組織圖 5
2.1.2 自我組織圖的增長 6
2.1.3 階層的產生 7
2.1.4 增長層級式自我組織圖 8
2.2 文件分群演算法 9
2.2.1 K-means演算法 9
2.2.2 貝氏分類 9
2.2.3 類神經網路分類 10
2.3 文本探勘 10
2.4 基於自我組織圖之文件分群演算法 14
第三章 研究方法與實驗設計 17
3.1 文件前置處理 18
3.1.1 斷詞 18
3.1.2 關鍵字選取. 19
3.1.3 文件向量產生 20
3.2 文件分群 22
3.2.1 SOM訓練 22
3.2.2 主題導向式自我組織圖 26
第四章 實驗結果與評估 36
4.1 實驗步驟 36
4.1.1 前置處理 37
4.1.2 資料分群. 40
4.2 實驗評估 49
第五章 結論與討論 60
5.1 結論 60
5.2 未來研究方向 61
參考文獻 62

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