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Clustering methods for big data analytics techniques, toolboxes and applications

Olfa Nasraoui, Chiheb-Eddine Ben N'Cir, editors (Springer Nature, 2019)

 Abstrak

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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 Metadata

No. Panggil : e20507207
Entri tambahan-Nama orang :
Subjek :
Penerbitan : Switzerland: Springer Nature, 2019
Sumber Pengatalogan: LibUI eng rda
Tipe Konten: text
Tipe Media: computer
Tipe Pembawa: online resource
Deskripsi Fisik: ix, 187 pages : illustration
Tautan: https://doi.org/10.1007/978-3-319-97864-2">
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No. Panggil No. Barkod Ketersediaan
e20507207 02-20-286961937 TERSEDIA
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