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研究生:林昇毅
研究生(外文):Lin, Sheng-Yi
論文名稱:使用叢集整合技術偵測主曲線之研究
論文名稱(外文):The Study of Principal Curve Detection Using Cluster Ensembles
指導教授:王才沛
指導教授(外文):Wang, Tsai-Pei
學位類別:碩士
校院名稱:國立交通大學
系所名稱:多媒體工程研究所
學門:電算機學門
學類:軟體發展學類
論文種類:學術論文
論文出版年:2009
畢業學年度:98
語文別:中文
論文頁數:43
中文關鍵詞:叢集整合偵測主曲線
外文關鍵詞:principal curve detection using cluster ensembles
相關次數:
  • 被引用被引用:0
  • 點閱點閱:184
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  • 下載下載:18
  • 收藏至我的研究室書目清單書目收藏:0
主曲線是通過資料中心的一條線,因此找到一個圖形的主曲線即可以得到一個圖形的基本形狀。在過去的研究當中,利用叢集化演算法來找尋主曲線在圖形識別的領域中是一個很熱門的議題,其中大部份的演算法大都分為三個主要的步驟: 叢集化演算法找到分群結果、連接由第一步驟所得到的各個分群獲得一個初始的主曲線、對初始主曲線作平滑化。然而這些過去所提出的演算法中,都因為叢集化演算法先天的一些限制而有所美中不足的地方,例如如何得到適當的分群個數,初始條件以及雜訊量對於叢集化過程的影響,等等。在此我們想引入一種技術--叢集整合技術,利用叢集整合的特性來降低初始件以及雜訊量對於叢集化過程的影響,以此得到更穩定的叢集化後的分群結果,再將此分群結果利用階層聚合演算法來得到最終的分群結果。
A principal curve is a curve that passes through the middle of the data distribution. As a result, we can obtain the basic shape of a data distribution by detecting its principal curve. The detection of principal curves through clustering algorithms has been a popular topic in past research. Most of these algorithms consist of three main steps: a clustering algorithm to partition the data, the linking of the clusters to obtain an initial principal curve, and the smoothing of the initial principal curve. However, these algorithms all have some limitations due to the underlying clustering algorithms. Examples of such limitations include how to determine a proper number of initial clusters, initializations, and the effect of noise, etc. In this thesis, our goal is to apply the technique of cluster ensembles to principal curve detection. The benefit of cluster ensembles is the reduced effect of initialization and noise, and this leads to more stable clustering results. The final partition into principal curves are obtained using hierarchical agglomeration algorithms.
第一章 簡介 1
1.1 研究動機 1
1.2 章節概要 3
第二章 文獻探討 4
2.1 主曲線的偵測 4
2.2 叢集整合演算法 6
第三章 叢集整合技術偵測主曲線 9
3.1 GUSTAFSON-KESSEL 叢集化演算法 11
3.2 階層聚合演算法獲得第二階段的分群 16
3.3 使用叢集整合技術得到第三階段的分群 21
3.4 獲得單一分群的資料集合 25
3.5 使用修改的FCT演算法獲得主曲線 26
3.6 整合數段主曲線獲得最終結果 30
第四章 實驗結果 31
4.1 具有多重物件的資料集合偵測 31
4.2 測試具有交叉的資料集合 33
4.3 英文字母主曲線的偵測 36
第五章 結論與未來展望 40
參考文獻 42
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