Metaheuristic clustering
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 se...
Bewaard in:
| Hoofdauteurs: | , , |
|---|---|
| Andere auteurs: | |
| Formaat: | Livre numérique |
| Taal: | Anglais |
| Gepubliceerd in: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Editie: | 1st ed. 2009. |
| Reeks: | Studies in Computational Intelligence
178 |
| Online toegang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Metaheuristic Clustering, Texte imprimé, 9783540921721 • Metaheuristic Clustering, Texte imprimé, 9783642100710 • Metaheuristic Clustering, Texte imprimé, 9783540922230 • Metaheuristic Clustering, Texte imprimé, 9783540921721 |
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| 041 | 0 | |a eng | |
| 082 | |a 519 | ||
| 100 | 1 | |a Das, Swagatam, |d 1980- | |
| 245 | 1 | 0 | |a Metaheuristic clustering |c by Swagatam Das, Ajith Abraham, Amit Konar ; edited by Janusz Kacprzyk. |
| 250 | |a 1st ed. 2009. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 178 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Metaheuristic Pattern Clustering An Overview Differential Evolution Algorithm: Foundations and Perspectives Modeling and Analysis of the Population-Dynamics of Differential Evolution Algorithm Automatic Hard Clustering Using Improved Differential Evolution Algorithm Fuzzy Clustering in the Kernel-Induced Feature Space Using Differential Evolution Algorithm Clustering Using Multi-objective Differential Evolution Algorithms Conclusions and Future Research | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
| 700 | 1 | |a Abraham, Ajith, |d 1968- |4 aut | |
| 700 | 1 | |a Konar, Amit. |4 aut | |
| 700 | 1 | |a Kacprzyk, Janusz, |d 1947- |4 pbd | |
| 776 | 0 | |t Metaheuristic Clustering |b Texte imprimé |z 9783540921721 | |
| 776 | 0 | |t Metaheuristic Clustering |b Texte imprimé |z 9783642100710 | |
| 776 | 0 | |t Metaheuristic Clustering |b Texte imprimé |z 9783540922230 | |
| 776 | 0 | |t Metaheuristic Clustering |b Texte imprimé |z 9783540921721 | |
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