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研究生:鐘晟航
研究生(外文):Sheng-Hang Jong
論文名稱:以資料間距為基礎搭配矩形分割的非監督式聚類分割法
論文名稱(外文):An unsupervised clustering approach based on its data distribution and rectangle division
指導教授:楊英魁楊英魁引用關係
指導教授(外文):Ying-Kuei Yang
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:2008
畢業學年度:96
語文別:中文
論文頁數:74
中文關鍵詞:非監督式聚類演算法矩形間距
外文關鍵詞:unsupervised clustering algorithmrectanglegap
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  • 被引用被引用:1
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本文的主要目的是延續以資料間距為基礎的非監督式聚類演算法,並提供了一個較佳的分割方式,避免在圓形分佈的切割方式下,將不屬於當前聚類的資料點給切割進來。接著再以適當的猜測方式決定初始聚類中心位置後,除了利用兩兩相鄰資料點間距大小的特徵來決定聚類切割處之外,並針對此切割範圍內的所有資料點,以單維度方向兩兩相鄰資料點的間距為特徵,選定為矩形的半長,做矩形切割。此做法可以節省多餘的切割空間,更利於延展型聚類的分類結果。最後,為了試驗本文所提出的方法,總共模擬了六組不同特性的資料樣本。在模擬過程中,除了將這六組試驗樣本以本文所提的演算法執行分類,並將Fuzzy c-Means演算法、以資料間距為基礎的非監督式聚類演算法的分類結果以圖示列出對照。模擬結果顯示,本文所提出的方法比起Fuzzy c-Means演算法、以資料間距為基礎的非監督式聚類演算法擁有更高的正確性。
The main purpose of this paper is extended based on “An unsupervised clustering approach based on its data distribution” to offer a better way of cutting apart between the cluster and its neighbors. The new method presented in this paper will cut apart clusters by rectangle division. Comparing with round division, rectangle division can save some space and makes the clustered result more correct when dividing tall and slender data sets. The algorithm presented in this paper has been implemented, analysed and tested on six data sets. The results show that the proposed algorithm has much better classified ability than the Fuzzy c-Means algorithm and the algorithm of “An unsupervised clustering approach based on its data distribution”.
論文摘要 I
Abstract II
誌謝 III
目錄 III
圖表目錄 VI
第1章 緒論
1.1 研究背景 1
1.2 研究目的 2
1.3 論文架構 3
第2章 聚類與聚類方法 4
2.1 識別能力與聚類法則 4
2.2 非監督式的聚類演算法 6
2.2.1 K-NN演算法 6
2.2.2 FCM演算法 11
2.2.3 以資料間距為基礎的非監督式聚類分割法 15
2.3 本章結論 18
第3章 以資料間距為基礎搭配矩形分割的聚類演算法 19
3.1 演算法構想 19
3.2 聚類中心位置之決定 20
3.3 資料間距的計算 26
3.4 矩形分割 29
3.5 演算法之效度評估函數 33
3.5.1 聚合度函數 33
3.5.2 分離度函數 39
3.5.3 目標函數 44
3.6 演算法的步驟說明 45
3.7 本章結論 48
第4章 模擬結果分析 49
4.1 試驗方法 49
4.2 模擬結果 50
4.3 本章結論 69
第5章 結論 71
參考文獻 73
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