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Clustering methods for big data anal...

Ben N'Cir, Chiheb-Eddine.

Clustering methods for big data analyticstechniques, toolboxes and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Clustering methods for big data analyticsedited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir.
其他題名:	techniques, toolboxes and applications /
其他作者:	Nasraoui, Olfa.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	ix, 187 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Big data.
電子資源:	https://doi.org/10.1007/978-3-319-97864-2
ISBN:	9783319978642$q(electronic bk.)

Clustering methods for big data analyticstechniques, toolboxes and applications /
Clustering methods for big data analyticstechniques, toolboxes and applications /[electronic resource] :edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir. - Cham :Springer International Publishing :2019. - ix, 187 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

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.

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.

ISBN: 9783319978642$q(electronic bk.)

Standard No.: 10.1007/978-3-319-97864-2doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45 / C587 2019

Dewey Class. No.: 005.7

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505 0 $a 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 $a 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.
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