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{ "item_title" : "Clustering Methods for Big Data Analytics", "item_author" : [" Olfa Nasraoui", "Chiheb-Eddine Ben n'Cir "], "item_description" : "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.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/31/997/863/3319978632_b.jpg", "price_data" : { "retail_price" : "169.99", "online_price" : "169.99", "our_price" : "169.99", "club_price" : "169.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Clustering Methods for Big Data Analytics|Olfa Nasraoui

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

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Overview

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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Details

  • ISBN-13: 9783319978635
  • ISBN-10: 3319978632
  • Publisher: Springer
  • Publish Date: November 2018
  • Dimensions: 9.21 x 6.14 x 0.5 inches
  • Shipping Weight: 1.01 pounds
  • Page Count: 187

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