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研究生： 李鴻聰
Hong-Chung Lee
論文名稱： 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
指導教授： 何錦文
Chin-Wen Ho
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度： 92
語文別： 英文
論文頁數： 31
中文關鍵詞： 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
外文關鍵詞： extreme conditions, politician graph, regular graph, clustering, Minimum cut tree, edge-connectivity
相關次數： 點閱：8  下載：0 
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    • 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.

    Contents 1 Introduction 1 2 Related Works 2 2.1 Maximum Flows and Minimum Cuts 2 2.1.1 Maximum Flows 2 2.1.2 Minimum Cuts 3 2.2 Flow Equivalent Trees and Minimum Cut Trees 4 2.2.1 Flow Equivalent Trees 4 2.2.2 Minimum Cut Trees 5 2.3 Two Algorithms for Constructing Minimum Cut Trees 8 2.3.1 Gomory and Hu’s Agorithm 8 2.3.2 Gusfield’s Agorithm 13 2.4 The Minimum Cut Tree Clustering Algorithm 14 2.5 Some Properties of Parametric Networks and Minimum Cut Tree Clustering Algorithm 16 2.5.1 Breakpoints 16 2.5.2 Critical Points 19 2.6 Extreme Graphs and Super Extreme Graphs 20 2.6.1 Extreme Graphs 20 2.6.2 Super Extreme Graphs 21 3 Main Results 23 3.1 A Criterion for Extreme Graphs 23 3.2 Politician Graphs 24 3.3 Regular Graphs 26 3.3.1 Cages 27 3.3.2 Connected Symmetric Graphs 27 4 Conclusions 29 5 Biliography 30 List of Figures Figure 2.1: An undirected network and three of its flow equivalent trees. (a) An undirected network. (b)-(d) Flow equivalent trees. 5 Figure 2.2: An undirected network and two of its minimum cut tree. (a) An undirected network. (b) A minimum cut tree. (c) A minimum cut tree. 7 Figure 2.3: Construction of a minimum cut tree of . Find a corresponding minimum cut on the left, and build up the minimum cut tree on the right. 12 Figure 2.4: The minimum cuts which do not cross each other. 12 Figure 2.5: A minimum cut tree and its corresponding MCT partition of when . (a) An undirected network . (b) A new network . (c) A minimum cut tree . (d) A MCT partition of . 15 Figure 2.6: Weights and Breakpoints. (a) Weight of cut. (b) Weight of minimum cut. 17 Figure 2.7: Graph with 5 vertices and unit weight on each edge. 20 Figure 2.8: Cycle graph . 22

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