Clustering Methods for Big Data Analytics Techniques, Toolboxes and Applications

Titre:

ISBN (Numéro international normalisé des livres):

9783319978642

Edition:

1st ed. 2019.

PRODUCTION_INFO:

Cham : Springer International Publishing : Imprint: Springer, 2019.

Description physique:

IX, 187 p. 63 illus., 31 illus. in color. online resource.

Collections:

Unsupervised and Semi-Supervised Learning,

Table des matières:

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.

Extrait:

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. .

Terme sujet:

Telecommunication.

Computational intelligence.

Data mining.

Quantitative research.

Pattern recognition systems.

Communications Engineering, Networks.

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Automated Pattern Recognition.

Auteur ajouté:

Nasraoui, Olfa.