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. . Read more...
Abstract:
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. Read more...
Author(s): Ben N'Cir, Chiheb-Eddine; Nasraoui, Olfa (eds.)
Series: Unsupervised and semi-supervised learning
Publisher: Springer
Year: 2019
Language: English
Pages: 192
Tags: Big data.;Cluster analysis.;Data mining.;COMPUTERS -- Data Processing.;Artificial intelligence.;Business mathematics & systems.;Pattern recognition.;Communications engineering -- telecommunications.;Communications Engineering, Networks.;Computational Intelligence.;Data Mining and Knowledge Discovery.;Big Data/Analytics.;Pattern Recognition.
Content: 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.
