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Clustering Methods for Big Data Analytics : Techniques, Toolboxes and Applications / edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Unsupervised and Semi-Supervised Learning | SpringerLink BücherPublisher: Cham : Springer International Publishing, 2019Description: Online-Ressource (IX, 187 p. 63 illus., 31 illus. in color, online resource)ISBN:
  • 9783319978642
Subject(s): Additional physical formats: 9783319978635 | 9783319978659 | Erscheint auch als: 978-3-319-97863-5 Druck-Ausgabe | Printed edition: 9783319978635 | Printed edition: 9783319978659 DDC classification:
  • 621.382
LOC classification:
  • TK1-9971
DOI: DOI: 10.1007/978-3-319-97864-2Online resources: Summary: This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validationSummary: Introduction -- Clustering large scale data -- Clustering heterogeneous data -- Distributed clustering methods -- Clustering structured and unstructured data -- Clustering and unsupervised learning for deep learning -- Deep learning methods for clustering -- Clustering high speed cloud, grid, and streaming data -- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis -- Large documents and textual data clustering -- Applications of big data clustering methods -- Clustering multimedia and multi-structured data -- Large-scale recommendation systems and social media systems -- Clustering multimedia and multi-structured data -- Real life applications of big data clustering -- Validation measures for big data clustering methods -- ConclusionPPN: PPN: 1038691788Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SEB | ZDB-2-SXE
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