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Bibliografická citace

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0 (hodnocen0 x )
BK
Chichester : John Wiley & Sons, 1999
x,289 s. : il.

objednat
ISBN 0-471-98864-2 (váz.)
Obsahuje ilustrace, předmluvu, úvod, dodatek, rejstřík
Bibliografie: s. 277-285
Clustery - analýza - učebnice vysokošk.
Fuzzy množiny - učebnice vysokošk.
000032517
Contents // Preface ix // Introduction 1 // 1 Basic Concepts 5 // 1.1 Analysis of data ... 5 // 1.2 Cluster analysis... 8 // 1.3 Objective function-based cluster analysis... 11 // 1.4 Fuzzy analysis of data... 17 // 1.5 Special objective functions... 20 // 1.6 A principal clustering algorithm... 28 // 1.7 Unknown number of clusters problem ... 31 // 2 Classical Fuzzy Clustering Algorithms 35 // 2.1 The fuzzy c-means algorithm... 37 // 2.2 The Gustafson-Kessel algorithm... 43 // 2.3 The Gath-Geva algorithm... 49 // 2.4 Simplified versions of GK and GG ... 54 // 2.5 Computational effort... 58 // 3 Linear and Ellipsoidal Prototypes 61 // 3.1 The fuzzy c-varieties algorithm... 61 // 3.2 The adaptive fuzzy clustering algorithm... 70 // 3.3 Algorithms by Gustafson/Kessel and Gath/Geva... 74 // 3.4 Computational effort... 75 // 4 Shell Prototypes 77 // 4.1 The fuzzy c-shells algorithm... 78 // 4.2 The fuzzy c-spherical shells algorithm ... 83 // 4.3 The adaptive fuzzy c-shells algorithm... . 86 // v // vi CONTENTS // 4.4 The fuzzy c-ellipsoidal shells algorithm... 92 // 4.5 The fuzzy c-ellipses algorithm... 99 // 4.6 The fuzzy c-quadric shells algorithm... 101 // 4.7 The modified FCQS algorithm ... 107 // 4.8 Computational effort... 113 // 5 Polygonal Object Boundaries 115 // 5.1 Detection of rectangles... 117 // 5.2 The fuzzy c-rectangular shells algorithm... 132 // 5.3 The fuzzy c-2-rectangular shells algorithm... 145 // 5.4 Computational effort... 155 // 6 Cluster Estimation Models 157
// 6.1 AO membership functions... 158 // 6.2 ACE membership functions... 159 // 6.3 Hyperconic clustering (dancing cones)... 161 // 6.4 Prototype defuzzification... 165 // 6.5 ACE for higher-order prototypes... 171 // 6.6 Acceleration of the Clustering Process... 177 // 6.6.1 Fast Alternating Cluster Estimation (FACE) ... 178 // 6.6.2 Regular Alternating Cluster Estimation (rACE) . . 182 // 6.7 Comparison: AO and ACE ... 183 // 7 Cluster Validity 185 // 7.1 Global validity measures... 188 // 7.1.1 Solid clustering validity measures... 188 // 7.1.2 Shell clustering validity measures... 198 // 7.2 Local validity measures ... 200 // 7.2.1 The compatible cluster merging algorithm... 201 // 7.2.2 The unsupervised FCSS algorithm... 207 // 7.2.3 The contour density criterion... 215 // 7.2.4 The unsupervised (M)FCQS algorithm... 221 // 7.3 Initialization by edge detection... 233 // 8 Rule Generation with Clustering 239 // 8.1 From membership matrices to // membership functions... 239 // 8.1.1 Interpolation... 240 // 8.1.2 Projection and cylindrical extension... 241 // 8.1.3 Convex completion... 243 // 8.1.4 Approximation... 244 // 8.1.5 Cluster estimation with ACE... 247 // CONTENTS // vii // 8.2 Rules for fuzzy classifiers... 248 // 8.2.1 Input space clustering... 249 // 8.2.2 Cluster projection... 250 // 8.2.3 Input output product space clustering... 261 // 8.3 Rules for function approximation... 261 // 8.3.1 Input ouput product space clustering... 261 // 8.3.2 Input space clustering...
266 // 8.3.3 Output space clustering... 268 // 8.4 Choice of the clustering domain... 268 // Appendix 271 // A.l Notation... 271 // A.2 Influence of scaling on the cluster partition ... 271 // A.3 Overview on FCQS cluster shapes ... 274 // A.4 Transformation to straight lines... 274 // References 277 // Index 286

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