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Data mining in time series and strea...

Bunke, Horst, (1949-)

Data mining in time series and streaming databases

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data mining in time series and streaming databaseseditors, Mark Last, Horst Bunke, Abraham Kandel.
other author:	Last, Mark.
Published:	Singapore :World Scientific,c2018.
Description:	1 online resource (196 p.) :ill. (some col.), col. ports
Subject:	Data mining.
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/10655#t=toc
ISBN:	9789813228047$q(electronic bk.)

Data mining in time series and streaming databases
Data mining in time series and streaming databases[electronic resource] /editors, Mark Last, Horst Bunke, Abraham Kandel. - 1st ed. - Singapore :World Scientific,c2018. - 1 online resource (196 p.) :ill. (some col.), col. ports - Series in machine perception and artificial intelligence,v. 831793-0839 ;. - Series in machine perception and artificial intelligence ;v. 74..

Includes bibliographical references and index.

"This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining. The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic."--

Electronic reproduction.
Singapore :
World Scientific,
[2017]

Mode of access: World Wide Web.

ISBN: 9789813228047$q(electronic bk.)Subjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / D38328 2018

Dewey Class. No.: 006.3/12

Data mining in time series and streaming databases
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050 0 4 $a QA76.9.D343 $b D38328 2018
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245 0 0 $a Data mining in time series and streaming databases $h [electronic resource] / $c editors, Mark Last, Horst Bunke, Abraham Kandel.
250 $a 1st ed.
260 $a Singapore : $b World Scientific, $c c2018.
300 $a 1 online resource (196 p.) : $b ill. (some col.), col. ports
490 1 $a Series in machine perception and artificial intelligence, $x 1793-0839 ; $v v. 83
504 $a Includes bibliographical references and index.
520 $a "This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining. The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic."-- $c Provided by publisher.
533 $a Electronic reproduction. $b Singapore : $c World Scientific, $d [2017]
538 $a Mode of access: World Wide Web.
588 $a Description based on online resource; title from PDF title page (viewed January 8, 2018)
650 0 $a Data mining. $3 184440
650 0 $a Big data. $3 609582
650 0 $a Streaming technology (Telecommunications) $3 219176
650 0 $a Electronic data processing $x Distributed processing. $3 182427
650 0 $a Querying (Computer science) $v Congresses. $3 387170
650 0 $a Electronic books. $3 242495
700 1 $a Last, Mark. $3 257835
700 1 $a Bunke, Horst, $d 1949- $3 840149
700 1 $a Kandel, Abraham. $3 203005
830 0 $a Series in machine perception and artificial intelligence ; $v v. 74. $3 575154
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/10655#t=toc