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Constrained clustering : advances in algorithms, theory, and applications

Record Type:	Electronic resources : monographic
Title Information:	advances in algorithms, theory, and applications
Secondary Intellectual Responsibility:	BasuSugato,
Secondary Intellectual Responsibility:	DavidsonIan, 1971-
Secondary Intellectual Responsibility:	WagstaffKiri Lou,
Place of Publication:	Boca Raton
Published:	CRC Press;
Year of Publication:	c2009
Description:	1 online resource (441 p.)ill :
Series:	Chapman & Hall/CRC data mining and knowledge discovery series
Subject:	Cluster analysis - Data processing -
Subject:	Data mining -
Subject:	Computer algorithms -
Online resource:	http://www.crcnetbase.com/isbn/978-1-58488-996-0
Notes:	"A Chapman & Hall book."
Summary:	Covers the capabilities and limitations of constrained clustering. This title presents various types of constraints for clustering, describes useful variations of the standard problem of clustering under constraints, and applies clustering with constraints to relational, bibliographic, and video data
ISBN:	9781584889977electronic bk.
ISBN:	1584889977electronic bk.
ISBN:	9781584889960hbk.
ISBN:	1584889969hbk.

Constrained clustering : advances in algorithms, theory, and applications
Constrained clustering : advances in algorithms, theory, and applications / edited by Sugato Basu, Ian Davidson, Kiri L. Wagstaff - Boca Raton : CRC Press, c2009. - 1 online resource (441 p.) ; ill. - (Chapman & Hall/CRC data mining and knowledge discovery series).
"A Chapman & Hall book."Description based on print version record.
Includes bibliographical references and index.
ISBN 9781584889977ISBN 1584889977ISBN 9781584889960ISBN 1584889969
Cluster analysisData miningComputer algorithms -- Data processing

Basu, Sugato

Constrained clustering : advances in algorithms, theory, and applications
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300 $a Description based on print version record
320 $a Includes bibliographical references and index
330 $a Covers the capabilities and limitations of constrained clustering. This title presents various types of constraints for clustering, describes useful variations of the standard problem of clustering under constraints, and applies clustering with constraints to relational, bibliographic, and video data
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