Clustering Methods for Big Data Analytics (Record no. 77248)

000 -LEADER
fixed length control field 04043nam a22006015i 4500
001 - CONTROL NUMBER
control field 978-3-319-97864-2
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801215224.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 181027s2019 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319978642
-- 978-3-319-97864-2
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
245 10 - TITLE STATEMENT
Title Clustering Methods for Big Data Analytics
Sub Title Techniques, Toolboxes and Applications /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2019.
300 ## - PHYSICAL DESCRIPTION
Number of Pages IX, 187 p. 63 illus., 31 illus. in color.
490 1# - SERIES STATEMENT
Series statement Unsupervised and Semi-Supervised Learning,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 -- Conclusion.
520 ## - SUMMARY, ETC.
Summary, etc 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 validation. .
700 1# - AUTHOR 2
Author 2 Nasraoui, Olfa.
700 1# - AUTHOR 2
Author 2 Ben N'Cir, Chiheb-Eddine.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-97864-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2019.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Telecommunication.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Quantitative research.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
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-- Communications Engineering, Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Analysis and Big Data.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2522-8498
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