Clustering Methods for Big Data Analytics Techniques, Toolboxes and Applications /

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 dete...

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Detaylı Bibliyografya
Müşterek Yazar: SpringerLink (Online service)
Diğer Yazarlar: Nasraoui, Olfa (Editör), Ben N'Cir, Chiheb-Eddine (Editör)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : 2019.
Imprint: Springer,
Edisyon:1st ed. 2019.
Seri Bilgileri:Unsupervised and Semi-Supervised Learning,
Konular:
Telecommunication.
Computational intelligence.
Data mining.
Quantitative research.
Pattern recognition systems.
Communications Engineering, Networks.
Computational Intelligence.
Data Mining and Knowledge Discovery.
Data Analysis and Big Data.
Automated Pattern Recognition.
Online Erişim:Full-text access
Diğer Bilgiler
Özet: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. .
Fiziksel Özellikler:IX, 187 p. 63 illus., 31 illus. in color. online resource.
ISBN:9783319978642
ISSN:2522-8498
DOI:10.1007/978-3-319-97864-2

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