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Data mining in time series and streaming databases editors, Mark Last, Horst Bunke, Abraham Kandel.

PublishedSingapore : World Scientific Publishing Co. Pte Ltd., ©2018
Detail1 online resource (196 p.) : ill. (some col.), col. ports
LinkAccess to full text is restricted to subscribers.
https://doi.org/10.1142/10655
SubjectData mining
Big data
Streaming technology (Telecommunications)
Electronic data processing --Distributed processing[+]
Querying (Computer science)
Electronic books
Added AuthorLast, Mark
Bunke, Horst, 1949-
Kandel, Abraham
ISBN9789813228047 (ebook), -- 9789813228030 (hbk.)
ContentsStreaming data mining with massive online analytics (MOA) -- Weightless neural modeling for mining data streams -- Ensemble classifiers for imbalanced and evolving data streams -- Consensus learning for sequence data -- Clustering-based classification of document streams with active learning -- Supporting the mining of big data by means of domain knowledge during the pre-mining phases -- Data analytics: industrial perspective & solutions for streaming data
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TagData
CallnumberQA76.9 D262 2018
TitleData mining in time series and streaming databases [electronic resource] / editors, Mark Last, Horst Bunke, Abraham Kandel
PublishedSingapore : World Scientific Publishing Co. Pte Ltd., ©2018
Detail1 online resource (196 p.) : ill. (some col.), col. ports
Bibliography noteIncludes bibliographical references and index
Table of contentStreaming data mining with massive online analytics (MOA) -- Weightless neural modeling for mining data streams -- Ensemble classifiers for imbalanced and evolving data streams -- Consensus learning for sequence data -- Clustering-based classification of document streams with active learning -- Supporting the mining of big data by means of domain knowledge during the pre-mining phases -- Data analytics: industrial perspective & solutions for streaming data
Abstract"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."-- Provided by publisher
Subject
Subject
Subject
Subject
Subject
Subject
Added AuthorLast, Mark
Added AuthorBunke, Horst, 1949-
Added AuthorKandel, Abraham
ISBN9789813228047 (ebook)
ISBN-- 9789813228030 (hbk.)
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24500$aData mining in time series and streaming databases$h[electronic resource] /$ceditors, Mark Last, Horst Bunke, Abraham Kandel.
260##$aSingapore :$bWorld Scientific Publishing Co. Pte Ltd.,$c©2018.
300##$a1 online resource (196 p.) :$bill. (some col.), col. ports
4900#$aSeries in machine perception and artificial intelligence ;$vv. 83
504##$aIncludes bibliographical references and index.
5050#$aStreaming data mining with massive online analytics (MOA) -- Weightless neural modeling for mining data streams -- Ensemble classifiers for imbalanced and evolving data streams -- Consensus learning for sequence data -- Clustering-based classification of document streams with active learning -- Supporting the mining of big data by means of domain knowledge during the pre-mining phases -- Data analytics: industrial perspective & solutions for streaming data.
520##$a"This compendium is a completely revised version of an earlier book, Data Mining in Time Series Databases, by the same editors. It provides a unique collection of new articles written by leading experts that account for the latest developments in the field of time series and data stream mining. The emerging topics covered by the book include weightless neural modeling for mining data streams, using ensemble classifiers for imbalanced and evolving data streams, document stream mining with active learning, and many more. In particular, it addresses the domain of streaming data, which has recently become one of the emerging topics in Data Science, Big Data, and related areas. Existing titles do not provide sufficient information on this topic."--$cProvided by publisher.
533##$aElectronic reproduction.$bSingapore :$cWorld Scientific,$d[2017].
538##$aMode of access: World Wide Web.
588##$aDescription based on online resource; title from PDF title page (viewed January 8, 2018).
650#0$aData mining.
650#0$aBig data.
650#0$aStreaming technology (Telecommunications).
650#0$aElectronic data processing$xDistributed processing.
650#0$aQuerying (Computer science).
650#0$aElectronic books.
7001#$aLast, Mark.
7001#$aBunke, Horst,$d1949-.
7001#$aKandel, Abraham.
85640$uhttp://www.worldscientific.com/worldscibooks/10.1142/10655#t=toc$zAccess to full text is restricted to subscribers.
8564#$uhttps://doi.org/10.1142/10655
TagData
TitleData mining in time series and streaming databases
SubjectBig data.
SubjectData mining.
SubjectElectronic books.
SubjectElectronic data processing--Distributed processing.
SubjectQuerying (Computer science).
SubjectStreaming technology (Telecommunications).
SubjectQA76.9
DescriptionIncludes bibliographical references and index.
DescriptionStreaming data mining with massive online analytics (MOA) -- Weightless neural modeling for mining data streams -- Ensemble classifiers for imbalanced and evolving data streams -- Consensus learning for sequence data -- Clustering-based classification of document streams with active learning -- Supporting the mining of big data by means of domain knowledge during the pre-mining phases -- Data analytics: industrial perspective & solutions for streaming data.
Description"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."--
DescriptionElectronic reproduction.
DescriptionMode of access: World Wide Web.
DescriptionDescription based on online resource; title from PDF title page (viewed January 8, 2018).
PublisherSingapore : World Scientific Publishing Co. Pte Ltd.,
ContributorBunke, Horst,
ContributorKandel, Abraham.
ContributorLast, Mark.
Date2018
Date©2018.
Typeno type provided
Identifier9789813228047
Identifierhttps://doi.org/10.1142/10655
Identifierhttp://www.worldscientific.com/worldscibooks/10.1142/10655#t=toc
Languageeng

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