Learning from Data Streams in Evolving Environments  Methods and Applications /  [electronic resource] : 
edited by Moamar Sayed-Mouchaweh. 
 - 1st ed. 2019. 
 - VIII, 317 p. 131 illus., 95 illus. in color.  online resource. 
 - Studies in Big Data,  41  2197-6511 ; .
 - Studies in Big Data,  41 .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783319898032 
<br/><br/><label>Standard No.: </label>10.1007/978-3-319-89803-2  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Telecommunication.<br/>Security systems.<br/>Data mining.<br/>Control engineering.<br/>Communications Engineering, Networks.<br/>Security Science and Technology.<br/>Data Mining and Knowledge Discovery.<br/>Control and Systems Theory.
<br/><br/><label>LC Class. No.: </label>TK5101-5105.9 
<br/><br/><label>Dewey Class. No.: </label>621.382