Overview
- Introduces and explores fuzzy collaborative intelligence and systems
- Contains case study examples and software that demonstrate the methods discussed in practical situations
- Useful as a teaching text for graduate students and a guide for professionals
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
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About this book
This book introduces the basic concepts of fuzzy collaborative forecasting and clustering, including its methodology, system architecture, and applications. It demonstrates how dealing with disparate data sources is becoming more and more popular due to the increasing spread of internet applications. The book proposes the concepts of collaborative computing intelligence and collaborative fuzzy modeling, and establishes several so-called fuzzy collaborative systems. It shows how technical constraints, security issues, and privacy considerations often limit access to some sources. This book is a valuable source of information for postgraduates, researchers and fuzzy control system developers, as it presents a very effective fuzzy approach that can deal with disparate data sources, big data, and multiple expert decision making.
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Keywords
Table of contents (6 chapters)
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Front Matter
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Back Matter
Reviews
“This slim book by Chen and Honda provides readers with new developments in collaborative forecasting and clustering through techniques based on fuzzy logic. … The book and included research results are highly specialized. However, as it is organized in a coherent way, it is inspiring for further research on the topic. …. the book is recommended for researchers in collaborative ML who want a quick look at collaboration mechanisms via special prediction and clustering techniques based on fuzzy logic.” (Corrado Mencar,Computing Reviews, June 14, 2021)
Authors and Affiliations
About the authors
Tin-Chih Toly Chen received the Ph. D. degree in industrial engineering from National Tsin Hua University. He is now a Distinguished Professor in the Department of Industrial Engineering and Management at National Chiao Tung University. His research interests include fuzzy and neural computing, competitiveness analysis, cloud manufacturing, operations research, semiconductor manufacturing, and ambient intelligence. Dr. Chen has published over one hundred papers in refereed journals, and is the recipient of several research and paper awards. Dr. Chen is the founding editor of International Journal of Fuzzy System Applications and the founding president of Ambient Intelligence Association of Taiwan. He has been the editor or guest editor of journals including Fuzzy Sets and Systems, Journal of Intelligent Manufacturing, International Journal of Advanced Manufacturing Technology, International Journal of Technology Management, Robotics and Computer-Integrated Manufacturing, and International Journal of Intelligent Systems.
Katsuhiro Honda received the B.E., M.E. and D.Eng. Degrees in industrial engineering from Osaka Prefecture University, Osaka, Japan, in 1997,
1999 and 2004, respectively. From 1999 to 2013, he was a Research Associate, Assistant Professor and Associate Professor at Osaka Prefecture University, where he is a Professor in the Department of Computer Sciences and Intelligent Systems. His research interests include hybrid techniques of fuzzy clustering and multivariate analysis, data mining with fuzzy data analysis and neural networks. He has published over 80 papers in refereed journals and has presented over 200 papers in refereed international conferences. He received the best paper awards at FUZZ-IEEE 2008 and SCIS&ISIS2016, publication award and paper awards from Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) in 2010 and 2002, 2011 and 2012, respectively. He has been the associateeditor or guest editor of International Journal of Knowledge Engineering and Soft Data Paradigms, Advances in Fuzzy Systems, Mathematical Problems in Engineering and Applied Spatial Analysis and Policy.
Katsuhiro Honda received the B.E., M.E. and D.Eng. Degrees in industrial engineering from Osaka Prefecture University, Osaka, Japan, in 1997,
1999 and 2004, respectively. From 1999 to 2013, he was a Research Associate, Assistant Professor and Associate Professor at Osaka Prefecture University, where he is a Professor in the Department of Computer Sciences and Intelligent Systems. His research interests include hybrid techniques of fuzzy clustering and multivariate analysis, data mining with fuzzy data analysis and neural networks. He has published over 80 papers in refereed journals and has presented over 200 papers in refereed international conferences. He received the best paper awards at FUZZ-IEEE 2008 and SCIS&ISIS2016, publication award and paper awards from Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) in 2010 and 2002, 2011 and 2012, respectively. He has been the associateeditor or guest editor of International Journal of Knowledge Engineering and Soft Data Paradigms, Advances in Fuzzy Systems, Mathematical Problems in Engineering and Applied Spatial Analysis and Policy.
Bibliographic Information
Book Title: Fuzzy Collaborative Forecasting and Clustering
Book Subtitle: Methodology, System Architecture, and Applications
Authors: Tin-Chih Toly Chen, Katsuhiro Honda
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-030-22574-2
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-22573-5Published: 27 June 2019
eBook ISBN: 978-3-030-22574-2Published: 14 June 2019
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: IX, 89
Number of Illustrations: 33 b/w illustrations, 9 illustrations in colour
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Artificial Intelligence, Data-driven Science, Modeling and Theory Building, Operations Research/Decision Theory, Simulation and Modeling