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標題: 以最鄰近相似度為基礎的穩健分群方法
A Nearest Neighbor Similarity based Method for Robust Clustering
作者: 卓俊佑
Cho, Chun-You
關鍵字: cluster analysis
nearest neighbor similarities
出版社: 電機工程學系所
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摘要: 在這篇論文中首先提出群集分析所常面臨的問題,而解決這些問題的能力也將成為評估群集方法優劣的性能指標。論文一開始回顧各種傳統群集方法的理論基礎,並檢視其中的涵義與優缺,進而形成發展群集方法的背景知識。而後介紹一套以相似度為基礎的穩健群集方法,分析其中的利弊得失從中得到新的啟蒙,為本篇論文搭起了一個基礎的架構。並利用先前學習的背景知識判斷此群集方法的成敗關鍵,加以改良或移除,進而形成一套脫穎而出的穩健分群方法,稱之為最鄰近相似度群集法。最後經由測試不同的資料型態並比較兩者結果的優劣,驗證了群集方法的確獲得大幅的改善,並克服了傳統群集方法所常面臨的問題。
This research first discusses several problems that often occur in conventional clustering analysis. The capability of solving these problems can be used as the performance index of clustering methods. Then basic theories of several conventional clustering methods are reviewed and the survey of advantages and disadvantages is included. Based on these discussions, we introduce a nearest neighbor similarity-based robust clustering method (NN-SCM) by discovering the fundamental structure of the data set. Finally, several different data sets are used to compare the performances of the proposed approach and the traditional similarity-based clustering method. Simulation results indicate that the proposed approach has better clustering performance with less computation time while avoiding most of the conventional clustering problems.
其他識別: U0005-2808200713134300
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