04006oam 2200505 450 991073729990332120230120070545.03-319-89803-510.1007/978-3-319-89803-2(MiAaPQ)EBC5481468(PPN)229501761(EXLCZ)99410000000532335920180728d2019 u| 0engLearning from Data Streams in Evolving Environments Methods and Applications /edited by Moamar Sayed-Mouchaweh1st ed.Cham Springer International Publishing Imprint: Springer20191 online resource (VIII, 317 p. 131 illus., 95 illus. in color.)Studies in Big Data,2197-6503 ;41Chapter1: 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.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.Electrical engineeringQuality controlReliabilityIndustrial safetyData miningControl engineeringCommunications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Quality Control, Reliability, Safety and Riskhttps://scigraph.springernature.com/ontologies/product-market-codes/T22032Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Control and Systems Theoryhttps://scigraph.springernature.com/ontologies/product-market-codes/T19010Electrical 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.Sayed-Mouchaweh Moamar7619089910737299903321Learning from Data Streams in Evolving Environments3459247UNINA