Cluster Analysis for Data Mining and System Identification (eBook)

János Abonyi, Balázs Feil (Autoren)

eBook Download: PDF

2007 | 2007
XVIII, 306 Seiten
Springer Basel (Verlag)
978-3-7643-7988-9 (ISBN)

Infos

The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.

Inhalt

Contents 5
Preface 9
Overview 9
Organization 16
Features 17
Acknowledgements 17
Classical Fuzzy Cluster Analysis 19
1.1 Motivation 19
1.2 Types of Data 22
1.3 Similarity Measures 23
1.4 Clustering Techniques 26
1.5 Fuzzy Clustering 35
1.6 Cluster Analysis of Correlated Data 50
1.7 Validity Measures 58
Visualization of the Clustering Results 64
2.1 Introduction: Motivation and Methods 64
2.2 Visualization of Fuzzy Clustering Results by Modified Sammon Mapping 76
2.3 Fuzzy Self-Organizing Map based on Regularized Fuzzy c- Means Clustering 84
Clustering for Fuzzy Model Identification – Regression 98
3.1 Introduction to Fuzzy Modelling 98
3.2 Takagi–Sugeno (TS) Fuzzy Models 103
3.3 TS Fuzzy Models for Nonlinear Regression 113
3.4 Fuzzy Regression Tree 132
3.5 Clustering for Structure Selection 150
Fuzzy Clustering for System Identification 158
4.1 Data-Driven Modelling of Dynamical Systems 159
4.2 Interpretable Semi-Mechanistic Fuzzy Models by Clustering, OLS and FIS Model Reduction 179
4.3 Model Order Selection of Nonlinear Input- Output Models 200
4.4 State-Space Reconstruction and Prediction of Chaotic Time Series 215
Fuzzy Model based Classifiers 242
5.1 Fuzzy Model Structures for Classification 244
5.2 Iterative Learning of Fuzzy Classifiers 249
5.3 Supervised Fuzzy Clustering for the Identification of Fuzzy Classifiers 254
5.4 Supervised Clustering based Fuzzy Partitioning for Fuzzy Decision Tree Induction 262
Segmentation of Multivariate Time-series 270
6.1 Mining Time-series Data 270
6.2 Time-series Segmentation 272
6.3 Fuzzy Cluster based Fuzzy Segmentation 278
6.4 Conclusions 290
Hermite Spline Interpolation 291
Bibliography 294
Index 315

Produkt-Details

• Erscheint lt. Verlag: 10.8.2007
• Zusatzinfo: XVIII, 306 p.
• Verlagsort: Basel
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Medizin / Pharmazie ► Allgemeines / Lexika
• Themenwelt: Technik
• Themenwelt: Wirtschaft
• Schlagworte: algorithms • Calculus • classification • cluster analysis • Clustering • clustering algorithm • Computer • Computer Science • Data Mining • Dynamische Systeme • fuzzy • linear optimization • Modeling • Process Engineering • Statistics
• ISBN-10: 3-7643-7988-X / 376437988X
• ISBN-13: 978-3-7643-7988-9 / 9783764379889