Metaheuristics for big data

by Dhaenens, Clarisse

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Title Statements

Full title :Metaheuristics for big data. / Clarisse Dhaenens, Laetitia Jourdan.

Edition Statements

Publication Date:©2016

Publication Statements

Publisher(s):ISTE, Wiley
Place of Publication:London [England]
Language:eng
Publication Date:©2016

The Physical Object

Description:1 online resource (216 pages) : : illustrations, graphs, tables.

Edition Identifiers

ISBN:9781119347583, 1119347580
oclc:on1017974814
LC Classification:TA174
Dewey Decimal:620.0042015118

Cover; Title Page ; Copyright ; Contents; Acknowledgments; Introduction; 1. Optimization and Big Data; 1.1. Context of Big Data; 1.1.1. Examples of situations; 1.1.2. Definitions; 1.1.3. Big Data challenges; 1.1.4. Metaheuristics and Big Data; 1.2. Knowledge discovery in Big Data; 1.2.1. Data mining versus knowledge discovery; 1.2.2. Main data mining tasks; 1.2.3. Data mining tasks as optimization problems; 1.3. Performance analysis of data mining algorithms ; 1.3.1. Context; 1.3.2. Evaluation among one or several dataset(s); 1.3.3. Repositories and datasets; 1.4. Conclusion.

2. Metaheuristics -- A Short Introduction2.1. Introduction; 2.1.1. Combinatorial optimization problems; 2.1.2. Solving a combinatorial optimization problem; 2.1.3. Main types of optimization methods; 2.2. Common concepts of metaheuristics; 2.2.1. Representation/encoding; 2.2.2. Constraint satisfaction; 2.2.3. Optimization criterion/objective function; 2.2.4. Performance analysis; 2.3. Single solution-based/local search methods; 2.3.1. Neighborhood of a solution; 2.3.2. Hill climbing algorithm; 2.3.3. Tabu Search; 2.3.4. Simulated annealing and threshold acceptance approach.

2.3.5. Combining local search app…

Subjects

Classification automatique (Statistique) Cluster analysis Combinatorial optimization Conception technique -- Modèles mathéma… Engineering design -- Mathematical mode… Optimisation combinatoire