臺灣博碩士論文加值系統

模糊C-平均分群法(Fuzzy C-Means, FCM)已經廣泛的應用在多種不同的地方，本研究提出一結合核心直觀模糊C-平均分群法與約略集合(Kernel Intuitionistic Fuzzy C means With Rough Set, KIFCMRS)，並將此方法應用於數位學習平台的資料分析上。主要分成兩個階段用以進行規則產生。第一階段透過KIFCM將有相關性的內容分成數組相似資料群，第二階段使用約略集合產生規則庫。最後比較不同的方法，第一階段先比較本研究提出的KIFCM和另外兩種方法(KM、FCM)，第二階段則是比較本研究提出的KIFCMRS和另外兩種方法(ID3、RS)。實驗結果顯示本研究所提出之KIFCMRS優於其他被提出比較之方法。

Fuzzy C-mean (FCM) algorithms have been widely used in variety of different places. This paper proposes a kernel intuitionistic fuzzy c-means clustering algorithms with rough set (KIFCMRS), and this method is applied to the E-learning data analysis. The rule generation can be divided into two stages for effective rule generation. In the first stage, KIFCM takes advantages of kernel function and intuitionistic fuzzy sets to cluster raw data into similarity groups. In the second stage, the rough set theory is employed to generate rules with different groups. Finally, this paper compared different methods, the first stages comparative KIFCM and the other two methods (KM, FCM), the second stages compare the KIFCMRS and the other two methods (ID3, RS). Comparison with other approaches demonstrate the superior performance of the proposed KIFCMRS.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章　緒論 1
1.1　研究背景 1
1.2　研究動機 1
1.3　研究目的 2
1.4　研究架構 2
第二章　文獻探討 4
2.1　數位學習 4
2.2　模糊理論與集合 5
2.3　直觀模糊集合理論 6
2.4　分群方法 9
2.4.1　K-means 10
2.4.2　Fuzzy C-means 13
2.5　約略集合 17
第三章　研究方法 30
3.1　核心直觀模糊分群法 31
3.2　使用約略集合產生分析結果 36
3.2.1　屬性化簡 36
3.2.2　最小屬性集合篩選 36
3.2.3　產生法則 37
3.2.4　計算覆蓋率 37
3.2.5　產生推薦表 37
第四章　實驗結果與討論 38
4.1　資料特性 38
4.2　分群法和分類法的測試實驗 39
4.3　實證實驗 43
4.4　T檢定 45
4.5　結果討論 47
第五章　結論 48
5.1　研究結論 48
5.2　未來方向 48
參考文獻 49
附錄 53

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