Clustering Methods for Big Data Analytics : Techniques, Toolboxes and Applications / / edited by Olfa Nasraoui, Chiheb-Eddine Ben N'Cir

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (IX, 187 p. 63 illus., 31 illus. in color.)
• Disciplina: 621.382
• Soggetto topico: Electrical engineering; Computational intelligence; Data mining; Big data; Pattern perception; Communications Engineering, Networks; Computational Intelligence; Data Mining and Knowledge Discovery; Big Data/Analytics; Pattern Recognition
• Persona (resp. second.): NasraouiOlfa; Ben N'CirChiheb-Eddine
• Nota di contenuto: 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.
• Sommario/riassunto: 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. .
• Titolo autorizzato: Clustering Methods for Big Data Analytics
• ISBN: 3-319-97864-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910337659403321
• Lo trovi qui: Univ. Federico II
• Serie: Unsupervised and Semi-Supervised Learning, . 2522-848X