研究生: |
鐘晟航 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 An unsupervised clustering approach based on its data distribution and rectangle division |
指導教授: |
楊英魁
Ying-Kuei Yang |
口試委員: |
黎碧煌
Bih-Hwang Lee 孫宗瀛 Tsung-Ying Sun 李建南 Chien-Nan Lee |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電機工程系 Department of Electrical Engineering |
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 algorithm, rectangle, gap |
相關次數: | 點閱:293 下載:0 |
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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”.
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