# 模糊聚类分析--分类，数据分析与图像识别方法(英文版)

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Fuzzy Cluster Analysis -Methods for Classification,Data Analysis and Image Recognition Frank Hoppner, Frank Klawonn, Rudolf Kruse,Thomas Runkler 著 目录 1 Basic Concepts 1.1 Analysis of data 1.2 Cluster analysis 1.3 Objective functilm-based cluster analysis 1.5 Special objective functions 1.6 A principal clustering algorithm 1. 7 Unknown number of clusters problem 2 Classical Fuzzy Clustering Algorithms 2.1 The fuzzy c-means algorithm 2.2 The Gustafson-Kessel algorithm 2.3 The Gath-Geva algorithm 2.4 Simplified versions of GK and GG 2.5 Computational effort 3 Linear and Ellipsoidal Prototypes 3.1 The fuzzy c-varieties algorithm 3.2 The adaptive fuzzy clustering algorithm 3.3 Algorithms by Gustafson/Kessel and Gath/ Geva 3.4 Computational effort 4 Shell Prototypes 4.1 The fuzzy c-shells algorithm 4.2 The fuzzy c-spherical shells algorithm 4.3 The adaptive fuzzy. c-shells algorithm 4.4 The fuzzy c-ellipsoidal shells algorithm 4.5 The fuzzy c-ellipses algorithm 4.6 The fuzzy c-quadric shells algorithm 4.7 The modified FCQS algorithm 4.8 Computational effort 5 Polygonal Object Boundaries 5.1 Detection of rectangles 5.2 The fuzzy c-rectangular shells algorithm 5.3 The fuzzy c-2-rectangular sl}ells algorithm 5.4 Computational effort 6 Cluster Estimation Models 6.1 AO membership functions 6.2 ACE membership functions 6.3 Hyperconic clustering (dancing cones) 6.4 Ptototype defuzzification. 6.5 ACE for higher-order prototypes 6.6 Acceleration of the Clustering Process 6.6.1 Fast Alternating Cluster Estimation (F.-\CE) 6.6.2 Regular Alternating Cluster Estimation (rACE) 6. 7 Comparison: AO and ACE 7 Cluster Validity 7.1 Global validity measures 7.1.1 Solid clustering validity measures 7.1.2 Shell clustering validity measures 7.2 Local validity measures 7.2.1 The compatible cluster merging algorithm 7.2.2 The unsupervised FCSS algorithm 7.2.3 The contour density criterion 7.2.4 T

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