國立虎尾科技大學 |

Partitional Clustering via Nonsmooth Optimization = Clustering via Optimization /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Partitional Clustering via Nonsmooth Optimization/ by Adil M. Bagirov, Napsu Karmitsa, Sona Taheri.
Reminder of title:	Clustering via Optimization /
Author:	M. Bagirov, Adil.
other author:	Karmitsa, Napsu.
Description:	XX, 336 p. 78 illus., 77 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Electrical engineering. -
Online resource:	https://doi.org/10.1007/978-3-030-37826-4
ISBN:	9783030378264

Partitional Clustering via Nonsmooth Optimization = Clustering via Optimization /
M. Bagirov, Adil.

Partitional Clustering via Nonsmooth OptimizationClustering via Optimization /[electronic resource] :by Adil M. Bagirov, Napsu Karmitsa, Sona Taheri. - 1st ed. 2020. - XX, 336 p. 78 illus., 77 illus. in color.online resource. - 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

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

596380
Electrical engineering.

LC Class. No.: TK1-9971

Dewey Class. No.: 621.382

Partitional Clustering via Nonsmooth Optimization = Clustering via Optimization /
LDR:02832nam a22004095i 4500 001 1025627
003 DE-He213
005 20200702082921.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030378264 $9 978-3-030-37826-4
024 7 $a 10.1007/978-3-030-37826-4 $2 doi
035 $a 978-3-030-37826-4
050 4 $a TK1-9971
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a M. Bagirov, Adil. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1321923
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.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XX, 336 p. 78 illus., 77 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Unsupervised and Semi-Supervised Learning, $x 2522-848X
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.
650 0 $a Electrical engineering. $3 596380
650 0 $a Pattern recognition. $3 1253525
650 0 $a Signal processing. $3 561459
650 0 $a Image processing. $3 557495
650 0 $a Speech processing systems. $3 564428
650 0 $a Artificial intelligence. $3 559380
650 0 $a Data mining. $3 528622
650 1 4 $a Communications Engineering, Networks. $3 669809
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Signal, Image and Speech Processing. $3 670837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Karmitsa, Napsu. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1321924
700 1 $a Taheri, Sona. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1321925
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030378257
776 0 8 $i Printed edition: $z 9783030378271
776 0 8 $i Printed edition: $z 9783030378288
830 0 $a Unsupervised and Semi-Supervised Learning, $x 2522-848X $3 1304411
856 4 0 $u https://doi.org/10.1007/978-3-030-37826-4
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)