04032nam 22006975 450 991048413840332120200701140551.03-030-13962-X10.1007/978-3-030-13962-9(CKB)4100000007810371(MiAaPQ)EBC5733064(DE-He213)978-3-030-13962-9(PPN)243767560(EXLCZ)99410000000781037120190316d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierStream Data Mining: Algorithms and Their Probabilistic Properties /by Leszek Rutkowski, Maciej Jaworski, Piotr Duda1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (331 pages)Studies in Big Data,2197-6503 ;563-030-13961-1 Introduction and Overview of the Main Results of the Book -- Basic concepts of data stream mining -- Decision Trees in Data Stream Mining -- Splitting Criteria based on the McDiarmid’s Theorem.This book presents a unique approach to stream data mining. Unlike the vast majority of previous approaches, which are largely based on heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to accommodate data streams; in this regard, new splitting criteria are developed to guarantee that they are asymptotically equivalent to the classical batch tree. Moreover, new decision trees are designed, leading to the original concept of hybrid trees. In turn, nonparametric techniques based on Parzen kernels and orthogonal series are employed to address concept drift in the problem of non-stationary regressions and classification in a time-varying environment. Lastly, an extremely challenging problem that involves designing ensembles and automatically choosing their sizes is described and solved. Given its scope, the book is intended for a professional audience of researchers and practitioners who deal with stream data, e.g. in telecommunication, banking, and sensor networks.Studies in Big Data,2197-6503 ;56Computational intelligenceData miningSignal processingImage processingSpeech processing systemsBig dataArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Data Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Signal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Big Data/Analyticshttps://scigraph.springernature.com/ontologies/product-market-codes/522070Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Data mining.Signal processing.Image processing.Speech processing systems.Big data.Artificial intelligence.Computational Intelligence.Data Mining and Knowledge Discovery.Signal, Image and Speech Processing.Big Data/Analytics.Artificial Intelligence.006.312Rutkowski Leszekauthttp://id.loc.gov/vocabulary/relators/aut477299Jaworski Maciejauthttp://id.loc.gov/vocabulary/relators/autDuda Piotrauthttp://id.loc.gov/vocabulary/relators/autBOOK9910484138403321Stream Data Mining: Algorithms and Their Probabilistic Properties2855504UNINA