Bagirov / Karmitsa / Taheri Partitional Clustering via Nonsmooth Optimization
2. Auflage 2025
ISBN: 978-3-031-76512-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Clustering via Optimization
E-Book, Englisch, 395 Seiten
Reihe: Unsupervised and Semi-Supervised Learning
ISBN: 978-3-031-76512-4
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
This updated 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 very large data and data with noise and outliers. 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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Introduction.- Introduction to Clustering.- Clustering Algorithms.- Nonsmooth Optimization Models in Cluster Analysis.- Nonsmooth Optimization.- Optimization based Clustering Algorithms.- Implementation and Numerical Results.- Conclusion.