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研究生： 林耿生
Keng-Sheng Lin
論文名稱： 植基於線對稱性質之有效的K均值分群演算法
An Efficient Line Symmetry-Based K-Means Algorithm
指導教授： 鍾國亮
Kuo-Liang Chung
口試委員: 顏文明
none
陳玲慧
none
蔡明忠
none
貝蘇章
none
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2005
畢業學年度： 93
語文別： 英文
論文頁數： 16
中文關鍵詞： 分群 、k均值分群演算法 、點對稱 、線對稱
外文關鍵詞： K-means_algorithm Clustering, Point-Symmetry Line
相關次數： 點閱：366  下載：2 
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  •  上一筆

    • 對於最新發表的點對稱距離量度，本篇論文將延伸此方法，進而提出一個植基於線對稱性質之有效的K均值分群演算法。與先前的點對稱K均值演算法相比，我們所提出的改良式線對稱K均值演算法對於不同的資料分佈具有較高的強健性。我們所提出的演算法不僅適用於資料點在對稱群內的關係同時也能適用於對稱群間的關係並根據一些自然界中真實資料集所實驗的結果顯示出我們所提出的改良式線對稱K均值演算法有令人滿意的效果。

    Recently, Su and Chou presented an efficient point symmetry--based
    K--means algorithm. Extending their point symmetry--based K--means
    algorithm, this paper presents a novel line symmetry--based K--means
    algorithm for clustering the data set with line symmetry property.
    Based on some real data sets, experimental results demonstrate that
    our proposed line symmetry--based K--means algorithm is rather
    encouraging.

    1. INTRODUCTION 1 2. THE PAST WORK BY SU AND BHOU 3 3. THE PROPOSED MODIFIED SYMMETRY SIMILARITY LEVEL OPERATOR 7 4. THE PROPOSED LINE SYMMETRY-BASED K-MEANS ALGORITHM 12 5. EXPERIMENTAL RESULTS 16 6. CONCLUSIONS 26 7. APPENDIX 27

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