Metaheuristic Clustering

de Swagatam Das
État : Neuf
151,56 €
TVA incluse - Livraison GRATUITE
Swagatam Das Metaheuristic Clustering
Swagatam Das - Metaheuristic Clustering

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Livraison : entre mardi 28 septembre 2021 et jeudi 30 septembre 2021
Vente et expédition: Dodax EU

La description

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention.


In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges.


Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

Contributeurs

Écrivain:
Swagatam Das
Ajith Abraham
Amit Konar

Détails du produit

Commentaire illustrations:
46 Tabellen
Remarks:

Latest research on metaheuristic clustering

Type de média:
Couverture rigide
Éditeur:
Springer Berlin
Biographie:
Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).
Évaluation:
From the reviews:
"In this volume, the performance of DE is illustrated, when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. ... The reader is carefully navigated through the efficacies of clustering, evolutionary optimization and a hybridization of the both." (T. Postelnicu, Zentralblatt MATH, Vol. 1221, 2011)
Langues:
Anglais
Nombre de pages:
252
Sommaire:

Cluster analysis means the organization of an unlabeled collection of objects into separate groups based on their similarity. With the use of several real world examples, this book formulates clustering as an optimization problem.


Données de base

Type d'produit:
Livre relié
Date de publication:
24 mars 2009
Dimensions du colis:
0.236 x 0.157 x 0.02 m; 0.544 kg
GTIN:
09783540921721
DUIN:
L6RPE6Q0SCS
MPN:
46 black & white tables
151,56 €
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