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Record Nr. |
UNINA9910483292903321 |
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Autore |
Chen Tin-Chih Toly |
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Titolo |
Fuzzy Collaborative Forecasting and Clustering : Methodology, System Architecture, and Applications / / by Tin-Chih Toly Chen, Katsuhiro Honda |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (99 pages) : illustrations |
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Collana |
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SpringerBriefs in Applied Sciences and Technology, , 2191-530X |
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Disciplina |
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Soggetti |
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Computational intelligence |
Data mining |
Artificial intelligence |
Sociophysics |
Econophysics |
Operations research |
Decision making |
Computer simulation |
Computational Intelligence |
Data Mining and Knowledge Discovery |
Artificial Intelligence |
Data-driven Science, Modeling and Theory Building |
Operations Research/Decision Theory |
Simulation and Modeling |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Fuzzy Collaborative Intelligence and Systems -- Linear Fuzzy Collaborative Forecasting Methods -- Nonlinear Fuzzy Collaborative Forecasting Methods -- Fuzzy Co-clustering -- Collaborative Framework for Fuzzy Co-clustering -- Three-mode Fuzzy Co-clustering -- Collaborative Framework for Three-mode Fuzzy Co-clustering. |
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Sommario/riassunto |
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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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