
Fuzzy Clustering Algorithms
Symbiotic Relationship Between Mathematics and Similarity Measures.
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Fuzzy Clustering Algorithms
Symbiotic Relationship Between Mathematics and Similarity Measures.
About this book
This book bridges theoretical concepts in fuzzy clustering with practical applications. It elucidates the mathematical foundations of fuzzy clustering methods, serving as a much-needed resource in the field. By explicating complex concepts, it will aid researchers, practitioners, and students in the analysis, design, and optimization of algorithms. This valuable resource will contributes to the utilization and enhancement of fuzzy clustering techniques across various domains. The book delves into the mathematical underpinnings, methods, and applications of fuzzy clustering. This book will enable academics, practitioners, and students to grasp the mathematical significance of fuzzy clustering in contemporary data analysis and to explore creative applications. It is an essential volume for data analysis professionals and students alike. Using advanced mathematical methods, it instructs readers on understanding, designing, and applying these algorithms. Its unique blend of theory and practice bridges the mathematical foundation with practical application, empowering readers to effectively analyze large datasets and uncover hidden patterns.
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Information
Table of contents
- Title Page
- Copyright
- Contents
- Frontmatter
- Dedication
- Contents
- About the authors
- Chapter 1 Introduction
- Chapter 2 Fuzzy C-means clustering algorithms
- Chapter 3 Possibilistic C-means
- Chapter 4 Robust fuzzy C-means (FCM-σ)
- Chapter 5 PCM-σ (robust possibilistic C-means)
- Chapter 6 Fuzzy possibilistic C-means (FPCM)
- Chapter 7 Possibilistic fuzzy C-means (PFCM)
- Index
- Index