Committee login






Small thumbnail

Reliability Investigation of LED Devices for Public Light Applications

Durability, Robustness and Reliability of Photonic Devices Set

Small thumbnail

Aerospace Actuators 2

Signal-by-Wire and Power-by-Wire

Small thumbnail

Flash Memory Integration

Performance and Energy Considerations

Small thumbnail

Mechanics of Aeronautical Solids, Materials and Structures

Small thumbnail

Engineering Investment Process

Making Value Creation Repeatable

Small thumbnail

Space Strategy

Small thumbnail

Distributed Systems

Concurrency and Consistency

Small thumbnail

Fatigue of Textile and Short Fiber Reinforced Composites

Durability and Ageing of Organic Composite Materials Set Volume 1

Small thumbnail

Management of the Effects of Coastal Storms

Policy, Scientific and Historical Perspectives

Small thumbnail

Computational Color Science

Variational Retinex-like Methods

Small thumbnail

Metaheuristics for Big Data

Metaheuristics set Volume 5

Clarisse Dhaenens and Laetitia Jourdan, University of Lille, France

ISBN: 9781848218062

Publication Date: August 2016   Hardback   212 pp.

105.00 USD

Add to cart


Ebook Ebook


Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts.
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.


1. Optimization and Big Data.
2. Metaheuristics A Short Introduction.
3. Metaheuristics and Parallel Optimization.
4. Metaheuristics and Clustering.
5. Metaheuristics and Association Rules.
6. Metaheuristics and (Supervised) Classification.
7. On the Use of Metaheuristics for Feature Selection in Classification.
8. Frameworks.

About the Authors

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.


DownloadTable of Contents - PDF File - 97 Kb

Related Titles

0.31511 s.