Clustering Methods for Big Data Analytics - Techniques, Toolboxes and Applications
od Springer International Publishing
Stan: Nowy
553,14 zł
Zawiera podatek VAT, darmowa dostawa
Podoba Ci się ten produkt? Przekaż dalej!
553,14 zł zawiera VAT
Dostępnych sztuk: 1 Dostępnych tylko 1 sztuk Dostępnych ponad 10 sztuk
Dostawa: między wtorek, 12 lipca 2022 a czwartek, 14 lipca 2022
Sprzedaż i wysyłka: Dodax
Opis
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.
Dalsza informacja
Adnotacja do ilustracji:
IX, 187 p. 63 illus., 31 illus. in color.
Spis treści:
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.
Redaktor:
Nasraoui, Olfa;Nasraoui
Ben N'Cir, Chiheb-Eddine;Ben N'Cir
Uwagi:
Includes the most recent and innovative advances in Big Data Clustering
Describes recent tools, techniques, and frameworks for Big Data Analytics
Introduces surveys, applications and case studies of Big Data clustering in Deep Learning, Blockchain, Cybersecurity, Data Streams, and Tensor graphs
Describes recent tools, techniques, and frameworks for Big Data Analytics
Introduces surveys, applications and case studies of Big Data clustering in Deep Learning, Blockchain, Cybersecurity, Data Streams, and Tensor graphs
Nośnik:
Twarda oprawa
Publisher:
Springer International Publishing
Biografie:
Olfa Nasraoui is the endowed Chair of e-commerce and the founding director of the Knowledge Discovery & Web Mining Lab at the University of Louisville, where she is also Professor in Computer Engineering & Computer Science. She received her Ph.D in Computer Engineering and Computer Science from the University of Missouri-Columbia in 1999. Her research interests are machine learning algorithms and systems with an emphasis on clustering algorithms, web mining, and recommender systems. She is the recipient of a National Science Foundation CAREER Award and a Best Paper Award for theoretical contributions In computational intelligence at the ANNIE conference.
Chiheb Eddine Ben N’cir received his Ph.D in Computer Science and Management from Higher Institute of Management, University of Tunis, in 2014. Currently, he is an Assistant Professor at the Higher School of Digital Economy (University of Manouba) since 2015 and member of LARODEC laboratory (University of Tunis). He is also a Big Data and Business Intelligence instructor at IBM North Africa and Middle East. His research interests concern unsupervised learning methods and data mining tools with a special emphasis on Big Data clustering, disjoint and non-disjoint partitioning, kernel methods, as well as many other related fields.
Chiheb Eddine Ben N’cir received his Ph.D in Computer Science and Management from Higher Institute of Management, University of Tunis, in 2014. Currently, he is an Assistant Professor at the Higher School of Digital Economy (University of Manouba) since 2015 and member of LARODEC laboratory (University of Tunis). He is also a Big Data and Business Intelligence instructor at IBM North Africa and Middle East. His research interests concern unsupervised learning methods and data mining tools with a special emphasis on Big Data clustering, disjoint and non-disjoint partitioning, kernel methods, as well as many other related fields.
Język:
Angielski
Edycja:
1st ed. 2019
Liczba stron:
187
Dane podstawowe
Rodzaj produktu:
Książka zszyta
Wymiary opakowania:
0.236 x 0.158 x 0.018 m; 0.4 kg
GTIN:
09783319978635
DUIN:
KOQJ5R58QET
553,14 zł