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研究生:劉建村
研究生(外文):Liu Chien-Tsun
論文名稱:以資料間距為基礎的模糊聚類分析法
論文名稱(外文):A Data-Gap-Based Fuzzy Clustering Approach
指導教授:楊英魁楊英魁引用關係
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:133
中文關鍵詞:聚類演算法分離度函數聚合度函數資料間距模糊C均值演算法
外文關鍵詞:clustering algorithmSeparation functionCompactness functiondata gapFuzzy C-means algorithm
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論文摘要
本論文之目的是希望能提出一套完整的聚類演算法,在不需事先指定聚類數目,完全以資料本身分佈為基礎的情況下加以分類,以達到令人滿意的分類結果。基於此一目的,本論文提出以資料間距配合目標函式的評估之聚類演算法,這是因為人類在分類的直覺上就是以資料間距來加以分類,二群資料的間距夠大則分成二類,若間距不夠大則分成一類,間距大小的判斷,本文是以目標函式來加以評估。整個演算法的架構是以二階段進行,第一階段以增加演算法效率為主,第二階段修正錯誤使分類結果更加正確。
關鍵字:非監督式聚類演算法,聚類方法,模糊C均值演算法,資料間距,聚合度,分離度。

目錄
誌謝 I
論文摘要 II
目錄 III
圖表目錄 V
第1章 緒論 1
1.1 研究動機 1
1.2 研究目的及方法 2
1.3 章節簡介 3
第2章 模糊聚類分析法 5
2.1 聚類方法 5
2.1.1 分割法(Partitioning method) 5
2.1.1.1 K-means演算法 6
2.1.1.2 K個最鄰近演算法(K-Nearest Neighbors Method) 7
2.1.1.3 FCM演算法(Fuzzy C-Means Algorithm) 10
2.1.2 階層式聚類方法(Hierarchical Clustering Methods) 15
2.1.2.1 CURE:Clustering Using Representatives 16
2.2 本章結論 20
第3章 以資料間距為基礎的模糊聚類分析法 22
3.1 演算法構想 22
3.2 聚類半徑的計算 23
3.3 演算法之效度評估函數 26
3.3.1 聚合度函數 27
3.3.2 分離度函數 34
3.3.3 目標函數 39
3.4 粗分類演算法 41
3.4.1 粗分類演算法的步驟說明 43
3.4.2 粗分類演算法結果分析 45
3.5 細分類演算法 63
3.5.1 子群形態一的處理程序 63
3.5.2 子群形態二的處理程序 66
3.5.3 子群形態三的處理程序 69
3.5.4 細分類演算法的步驟說明 73
3.6 時間複雜度 74
第4章 模擬結果與分析 81
4.1 模擬方法 81
4.2 模擬結果 82
4.2.1 資料樣本1 83
4.2.2 資料樣本2 87
4.2.3 資料樣本3 91
4.2.4 資料樣本4 95
4.2.5 資料樣本5 99
4.2.6 資料樣本6 103
4.2.7 資料樣本7 107
4.2.8 資料樣本8 112
4.3 模擬結果分析 115
第5章 結論與展望 117
5.1 結論 117
5.2 未來展望與建議 119
參考文獻 121

參考文獻
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