Partitional Clustering via Nonsmooth Optimization
Clustering via Optimization
Springer Nature Switzerland
ISBN 978-3-030-37826-4
Standardpreis
Bibliografische Daten
eBook. PDF
2020
XX, 336 p. 78 illus., 77 illus. in color..
In englischer Sprache
Umfang: 336 S.
Verlag: Springer Nature Switzerland
ISBN: 978-3-030-37826-4
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Unsupervised and Semi-Supervised Learning
Produktbeschreibung
This book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the ¿eld and it is well suited for practitioners already familiar with the basics of optimization.
- Provides a comprehensive description of clustering algorithms based on nonsmooth and global optimization techniques
- Addresses problems of real-time clustering in large data sets and challenges arising from big data
- Describes implementation and evaluation of optimization based clustering algorithms
Autorinnen und Autoren
Produktsicherheit
Hersteller
Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg, DE
ProductSafety@springernature.com