Learning from Data Streams in Evolving Environments : Methods and Applications / / edited by Moamar Sayed-Mouchaweh

• Pubblicazione: Cham, : Springer International Publishing, : Imprint : Springer, 2019
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (VIII, 317 p. 131 illus., 95 illus. in color.)
• Soggetto topico: Electrical engineering; Quality control; Reliability; Industrial safety; Data mining; Control engineering; Communications Engineering, Networks; Quality Control, Reliability, Safety and Risk; Data Mining and Knowledge Discovery; Control and Systems Theory
• Altri autori: Sayed-MouchawehMoamar
• Nota di contenuto: Chapter1: Transfer Learning in Non-Stationary Environments -- Chapter2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Chapter3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Chapter4: Error-bounded Approximation of Data Stream: Methods and Theories -- Chapter5: Ensemble Dynamics in Non-stationary Data Stream Classification -- Chapter6: Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Chapter7: Large-scale Learning from Data Streams with Apache SAMOA -- Chapter8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Chapter9: Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Chapter10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Chapter11: A Survey of Methods of Incremental Support Vector Machine Learning -- Chapter12: On Social Network-based Algorithms for Data Stream Clustering.
• Sommario/riassunto: This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.
• Titolo autorizzato: Learning from Data Streams in Evolving Environments
• ISBN: 3-319-89803-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910737299903321
• Lo trovi qui: Univ. Federico II