| Description |
1 online resource (xviii, 303 pages) : illustrations |
| Contents |
Cover -- TOC36;Contents -- Preface -- CH36;1 Classical Fuzzy Cluster Analysis -- 146;1 Motivation -- 146;2 Types of Data -- 146;3 Similarity Measures -- 146;4 Clustering Techniques -- 146;446;1 Hierarchical Clustering Algorithms -- 146;446;2 Partitional Algorithms -- 146;5 Fuzzy Clustering -- 146;546;1 Fuzzy partition -- 146;546;2 The Fuzzy c45;Means Functional -- 146;546;3 Ways for Realizing Fuzzy Clustering -- 146;546;4 The Fuzzy c45;Means Algorithm -- 146;546;5 Inner45;Product Norms -- 146;546;6 GustafsonKessel Algorithm -- 146;546;7 GathGeva Clustering Algorithm -- 146;6 Cluster Analysis of Correlated Data -- 146;7 Validity Measures -- CH36;2 Visualization of the Clustering Results -- 246;1 Introduction58; Motivation and Methods -- 246;146;1 Principal Component Analysis -- 246;146;2 Sammon Mapping -- 246;146;3 Kohonen Self45;Organizing Maps -- 246;2 Fuzzy Sammon Mapping -- 246;246;1 Modified Sammon Mapping -- 246;246;2 Application Examples -- 246;246;3 Conclusions -- 246;3 Fuzzy Self45;Organizing Map -- 246;346;1 Regularized Fuzzy c45;Means Clustering -- 246;346;2 Case Study -- 246;346;3 Conclusions -- CH36;3 Clustering for Fuzzy Model Identification Regression -- 346;1 Introduction to Fuzzy Modelling -- 346;2 TakagiSugeno 40;TS41; Fuzzy Models -- 346;246;1 Structure of Zero45; and First45;order TS Fuzzy Models -- 346;246;2 Related Modelling Paradigms -- 346;3 TS Fuzzy Models for Nonlinear Regression -- 346;346;1 Fuzzy Model Identification Based on GathGeva Clustering -- 346;346;2 Construction of Antecedent Membership Functions -- 346;346;3 Modified GathGeva Clustering -- 346;346;4 Selection of the Antecedent and Consequent Variables -- 346;346;5 Conclusions -- 346;4 Fuzzy Regression Tree -- 346;446;1 Preliminaries -- 346;446;2 Identification of Fuzzy Regression Trees based on Clustering Algorithm -- 346;446;3 Conclusions -- 346;5 Clustering for Structure Selection -- 346;546;1 Introduction -- 346;546;2 Input Selection for Discrete Data -- 346;546;3 Fuzzy Clustering Approach to Input Selection -- 346;546;4 Examples -- 346;546;5 Conclusions -- CH36;4 Fuzzy Clustering for System Identification -- 446;1 Data45;Driven Modelling of Dynamical Systems -- 446;146;1 TS Fuzzy Models of SISO and MIMO Systems -- 446;146;2 Clustering for the Identification of MIMO Processes -- 446;146;3 Conclusions -- 446;2 Semi45;Mechanistic Fuzzy Models -- 446;246;1 Introduction to Semi45;Mechanistic Modelling -- 446;246;2 Structure of the Semi45;Mechanistic Fuzzy Model -- 446;246;3 Clustering45;based Identification of the Semi45;Mechanistic Fuzzy Model -- 446;246;4 Conclusions -- 446;3 Model Order Selection -- 446;346;1 Introduction -- 446;346;2 FNN Algorithm -- 446;346;3 Fuzzy Clustering based FNN -- 446;346;4 Cluster Analysis based Direct Model Order Estimation -- 446;346;5 Application Examples -- 446;346;6 Conclusions -- 446;4 State45;Space Reconstruction -- 446;446;1 Introduction -- 446;446;2 Clustering45;based Approach to State45;space Reconstruction -- 446;446;3 Application Examples and Discussion -- 446;446;4 Case Stu |
| Summary |
Identifies fuzzy cluster analysis as a good approach to solving complex data mining and system identification problems. This work illustrates how advanced fuzzy clustering algorithms can be used for partitioning of the data, and also for visualization, regression, classification and time-series analysis |
| Bibliography |
Includes bibliographical references (pages 279-299) and index |
| Notes |
Print version record |
| In |
Springer e-books |
| Subject |
Cluster analysis.
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Data mining.
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System identification.
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Data mining.
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System identification.
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Cluster analysis.
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Cluster analysis
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Data mining
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System identification
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| Form |
Electronic book
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| Author |
Feil, Balázs.
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| ISBN |
9783764379889 |
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376437988X |
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3764379871 |
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9783764379872 |
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