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Fuzzy Collaborative Forecasting and Clustering : Methodology, System Architecture, and Applications / / by Tin-Chih Toly Chen, Katsuhiro Honda



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Autore: Chen Tin-Chih Toly Visualizza persona
Titolo: Fuzzy Collaborative Forecasting and Clustering : Methodology, System Architecture, and Applications / / by Tin-Chih Toly Chen, Katsuhiro Honda Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Edizione: 1st ed. 2020.
Descrizione fisica: 1 online resource (99 pages) : illustrations
Disciplina: 001.53
511.3223
Soggetto topico: 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
Persona (resp. second.): HondaKatsuhiro
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Fuzzy Collaborative Forecasting and Clustering  Visualizza cluster
ISBN: 3-030-22574-7
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910483292903321
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Serie: SpringerBriefs in Applied Sciences and Technology, . 2191-530X