最小切割樹群聚演算法極端情形之研究; A Study on Extreme Conditions of Minimum Cut Tree Clustering Algorithm

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Title:	最小切割樹群聚演算法極端情形之研究;A Study on Extreme Conditions of Minimum Cut Tree Clustering Algorithm
Authors:	李鴻聰;Hong-Chung Lee
Contributors:	資訊工程研究所
Keywords:	政治圖形;正規圖形;群聚演算法;最小切割樹;極端情形;邊連通;extreme conditions;politician graph;regular graph;clustering;Minimum cut tree;edge-connectivity
Date:	2003-12-29
Issue Date:	2009-09-22 11:34:40 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	群聚分析對於分析檢視資料之間的複雜結構是一種非常有效的工具，其應用領域相當廣泛，含蓋了生物資訊、影像處理和商業交易等範圍。群聚分析根據某些法則，比如最短距離或最小切割，來對資料作分群，使得同一群內的元素間關聯性強而群與群之間的關聯弱。在此篇論文中，我們特別探討由G.W. Flake等人所提出的群聚演算法──最小切割樹演算法，並分析出此演算法對某些圖形無法作分群，亦即分群的結果就只有兩種可能：一群或群，其中為圖形的點數。我們得到一滿足此現像的充分必要條件，並且發現某些圖形滿足這個條件。 Clustering algorithms are effective tools for exploring the structure of complex data sets. There are a lot of applications for clustering algorithms, including bioinformatics, image recognition, business transactions, etc. The minimum cut tree clustering algorithm is using maximum flow techniques to cluster the data (graphs). We prove that two kinds of graph, i.e. graph with edge connectivity and a graph with a vertex connected to every other vertex, are the extreme conditions in the algorithm.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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