臺灣博碩士論文加值系統

隨著電腦的普及和網際網路的蓬勃發展以及知識時代的到來，數位化文件的數量跟著爆發性的成長。在搜尋引擎上輸入關鍵字後的回傳結果往往數以萬計，從大量的文件資料庫中找尋出符合需求的文件也成為越來越費時的任務。因此，人們開始研究如何從龐大的資料集中找出符合需求的文件，而如何對數位化文件進行自動化的分類整理也成為管理文件資料庫上的一個重要議題。
近年來利用正規化概念分析(Formal Concept Analysis, FCA)於資訊擷取上的研究日益蓬勃。但由於傳統正規概念分析難以表現文件分類此種不明確的資訊(Tho et al., 2006)，因此有學者提出結合模糊理論於正規概念分析的模糊正規概念分析(Fuzzy FCA, FFCA) (Burusco and Fuentes-Gonzales, 1994)。此後，模糊正規概念分析的相關研究更如雨後春筍般地迅速發展。本研究試圖藉由資訊擷取技術（Information Retrieval）的方法讓機器自動對文件集進行分析，藉以挑選出整個文件集中最重要的數個關鍵字，並利用資訊擷取技術方法給予模糊隸屬度，再利用模糊正規化概念分析(Fuzzy Formal Concept Analysis, FFCA)方法對這些文件進行分類。本研究利用正規化概念分析所產生之概念網絡進行文件分類的計算推理，試圖尋找概念網絡圖除表現領域知識架構外的其他應用。期望能對於以正規化概念分析進行分類的研究乃至於正規化概念分析方法有所改善及幫助。
研究結果證實藉由正規概念分析所產生之概念網路圖輔以模糊數學方法確能得到準確的分類效果。在多個類別的分類上也能取得較為穩定的結果。

As computer becomes popular, the internet developes and the coming of the age of knowledge, the numerious of digital documents increases faster. There are always a huge deal of search resoult when we use search engine on the internet, and it becomes more and more difficult to find specified document from databases. Hense, people starts to find the way to find required documents from a huge database. Thus, automatical categorization of documennts becomes an important issue in managing document datas.
In recent years, more and more research uses formal concept analysis(FCA) on information retrieval. However, classical formal concept analysis present the fuzzy information of document categorization (Tho et al., 2006), some research thus combines fuzzy theory with FCA to fuzzy FCA (Burusco and Fuentes-Gonzales, 1994). The researches of FCA then become more and more. This proposed research is trying to analysis documents with information retrieval technology to find the most important keywords of the specified dataset, then give fuzzy membership degree and then categorize the documents with fuzzy FCA. In this research, the categorization is computed with the concept lattice produced from the FCA process to find an application of the concept lattice besides presenting the domain knowledge. We hope this to be helpful to the researches of document categorization using FCA.
The result shows that the categorization using concept lattice combining with fuzzy logic is precise. And the result is steady for all categories.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究步驟與流程 2
1.4 論文架構 3
1.5 研究限制 4
第二章 文獻回顧 5
2.1 資訊擷取 5
2.1.1 逆向一致頻率 5
2.1.2 一致性 6
2.1.3 詞頻-逆向文件頻率 6
2.1.4 文件分類技術 8
2.2 模糊邏輯 9
2.2.1 模糊α-截集 10
2.2.2 標準模糊運算元 12
2.2.3 模糊合成運算 12
2.2.4 模糊集合相似度 13
2.3 正規概念分析 13
2.3.1 概念背景 14
2.3.2 概念網絡 14
2.3.3 概念學習 16
2.3.4 模糊正規概念分析 18
第三章 研究方法 19
3.1 概念學習 19
3.1.1 特徵擷取 21
3.1.2 建構概念網絡 22
3.1.3 建構關鍵字-類別關聯矩陣 25
3.2 新文件分類 26
3.2.1 尋求最似概念 27
3.2.2 求取最佳分類 29
第四章 實驗與分析 31
4.1 實驗方法 31
4.2 實驗結果 31
4.3 敏感度分析 33
4.4 實驗結果比較 39
4.5 各分類方法分析 43
4.5.1 K-NN 43
4.5.2 決策樹、決策表 45
4.5.3 類神經網路(支援向量機，SVM) 45
4.5.4 貝氏網路 46
4.5.5 模糊正規概念分析法 47
第五章 結論與未來展望 48
5.1 結論 48
5.2 應用價值 49
5.3 未來展望 49
參考文獻 51

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