Fuzzy clustering analysis and its applications

Author: Gao Xinbo
Publisher:
Publish Date: 2004-01-01
Features: This book can serve as a textbook for Ph.D. and M.S. students, as well as senior undergraduate students in computer science, automatic control, signal and information processing, circuits and systems, systems engineering, and other related fields of science and engineering. It also provides significant reference value for researchers and engineering professionals in relevant fields. Fuzzy clustering analysis is an important branch of unsupervised pattern recognition and has broad applications in pattern recognition, data mining, computer vision, and fuzzy control, among other areas. It has also become a rapidly developing research hotspot in recent years. This book systematically discusses the fundamental theories and methods of fuzzy clustering analysis based on objective functions, as well as many open-ended problems and preliminary research findings. The main contents include: the theoretical foundations of fuzzy mathematics and possibility theory, hierarchical clustering, equivalence-based clustering, and graph-theoretic clustering methods; c-means-based objective function fuzzy clustering methods and their existing issues; fuzzy clustering neural networks; fuzzy clustering genetic algorithms and evolutionary strategies; prototype initialization methods for fuzzy clustering; effectiveness analysis of fuzzy clustering; clustering trend analysis of fuzzy clustering; fuzzy clustering analysis of interval-valued data and its extensions; as well as the application of fuzzy clustering in image segmentation and pattern recognition.