Clustering Methods for Big Data Analytics

Techniques, Toolboxes and Applications

Häftad, Engelska, 2019

AvOlfa Nasraoui,Chiheb-Eddine Ben N'Cir,Chiheb-Eddine Ben n'Cir

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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.

Produktinformation

Utgivningsdatum2019-01-19
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieUnsupervised and Semi-Supervised Learning
Antal sidor187
FörlagSpringer Nature Switzerland AG
ISBN9783030074197

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Elektronik och kommunikationer inom Naturvetenskap och teknik
Artificiell intelligens inom Data och it
Databaser inom Data och it

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.

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.