Clustering Methods for Big Data Analytics

Techniques, Toolboxes and Applications

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Éditeur :

Springer

Paru le : 2018-10-27

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 dete...
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Éditeur

Collection
n.c

Parution
2018-10-27

Pages
187 pages

EAN papier
9783319978635

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.

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EAN PDF
9783319978642
Prix
147,69 €
Nombre pages copiables
1
Nombre pages imprimables
18
Taille du fichier
6533 Ko
EAN EPUB
9783319978642
Prix
147,69 €
Nombre pages copiables
1
Nombre pages imprimables
18
Taille du fichier
14213 Ko

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