Metaheuristics for Big Data
The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.
Clarisse DHAENENS is Professor at the University of Lille in France and belongs to a research team working with both CRIStAL Laboratory (UMR CNRS) and Inria.
Laetitia JOURDAN is Professor at the University of Lille in France and belongs to a research team working with both CRIStAL Laboratory (UMR CNRS) and Inria.