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Partitional clustering via nonsmooth...

Bagirov, Adil.

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Partitional clustering via nonsmooth optimization = clustering via optimization /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Partitional clustering via nonsmooth optimization/ by Adil Bagirov, Napsu Karmitsa, Sona Taheri.
Reminder of title:	clustering via optimization /
Author:	Bagirov, Adil.
other author:	Karmitsa, Napsu.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xx, 395 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Introduction -- Introduction to Clustering -- Clustering Algorithms -- Nonsmooth Optimization Models in Cluster Analysis -- Nonsmooth Optimization -- Optimization based Clustering Algorithms -- Implementation and Numerical Results -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Nonsmooth optimization. -
Online resource:	https://doi.org/10.1007/978-3-031-76512-4
ISBN:	9783031765124

Partitional clustering via nonsmooth optimization = clustering via optimization /
Bagirov, Adil.

Partitional clustering via nonsmooth optimizationclustering via optimization /[electronic resource] :by Adil Bagirov, Napsu Karmitsa, Sona Taheri. - Second edition. - Cham :Springer Nature Switzerland :2025. - xx, 395 p. :ill. (chiefly color), digital ;24 cm. - Unsupervised and semi-supervised learning,2522-8498. - Unsupervised and semi-supervised learning..

Introduction -- Introduction to Clustering -- Clustering Algorithms -- Nonsmooth Optimization Models in Cluster Analysis -- Nonsmooth Optimization -- Optimization based Clustering Algorithms -- Implementation and Numerical Results -- Conclusion.

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 ﬁeld and it is well suited for practitioners already familiar with the basics of optimization. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI.

ISBN: 9783031765124

Standard No.: 10.1007/978-3-031-76512-4doiSubjects--Topical Terms:

709245
Nonsmooth optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 515.64

Partitional clustering via nonsmooth optimization = clustering via optimization /
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245 1 0 $a Partitional clustering via nonsmooth optimization $h [electronic resource] : $b clustering via optimization / $c by Adil Bagirov, Napsu Karmitsa, Sona Taheri.
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490 1 $a Unsupervised and semi-supervised learning, $x 2522-8498
505 0 $a Introduction -- Introduction to Clustering -- Clustering Algorithms -- Nonsmooth Optimization Models in Cluster Analysis -- Nonsmooth Optimization -- Optimization based Clustering Algorithms -- Implementation and Numerical Results -- Conclusion.
520 $a 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 ﬁeld and it is well suited for practitioners already familiar with the basics of optimization. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI.
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