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Partitional clustering via nonsmooth...

Bagirov, Adil M.

Partitional clustering via nonsmooth optimizationclustering via optimization /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Partitional clustering via nonsmooth optimizationby Adil M. Bagirov, Napsu Karmitsa, Sona Taheri.
Reminder of title:	clustering via optimization /
Author:	Bagirov, Adil M.
other author:	Karmitsa, Napsu.
Published:	Cham :Springer International Publishing :2020.
Description:	xx, 336 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Nonsmooth optimization.
Online resource:	https://doi.org/10.1007/978-3-030-37826-4
ISBN:	9783030378264$q(electronic bk.)

Partitional clustering via nonsmooth optimizationclustering via optimization /
Bagirov, Adil M.

Partitional clustering via nonsmooth optimizationclustering via optimization /[electronic resource] :by Adil M. Bagirov, Napsu Karmitsa, Sona Taheri. - Cham :Springer International Publishing :2020. - xx, 336 p. :ill. (some col.), digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - 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 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 ﬁeld 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.

ISBN: 9783030378264$q(electronic bk.)

Standard No.: 10.1007/978-3-030-37826-4doiSubjects--Topical Terms:

229311
Nonsmooth optimization.

LC Class. No.: QA402.5 / .B345 2020

Dewey Class. No.: 519.6

Partitional clustering via nonsmooth optimizationclustering 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 M. Bagirov, Napsu Karmitsa, Sona Taheri.
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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 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 ﬁeld 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.
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