Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/1630
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dc.contributor.authorKim, Jinho. ;en_US
dc.contributor.authorShim, Kyuseok. ;en_US
dc.contributor.authorCao, Longbing. ;en_US
dc.contributor.authorLee, Jae-Gil. ;en_US
dc.contributor.authorLin, Xuemin. ;en_US
dc.contributor.authorMoon, Yang-Sae. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:30:29Z-
dc.date.available2020-05-17T08:30:29Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319575292 ;en_US
dc.identifier.isbn9783319575285 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1630-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319575285. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction. ;en_US
dc.description.statementofresponsibilityedited by Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, Yang-Sae Moon.en_US
dc.description.tableofcontentsClassification and deep learning -- Social network and graph mining -- Privacy-preserving mining and security/risk applications -- Spatio-temporal and sequential data mining -- Clustering and anomaly detection -- Recommender system -- Feature selection -- Text and opinion mining -- Clustering and matrix factorization -- Dynamic, stream data mining -- Novel models and algorithms -- Behavioral data mining -- Graph clustering and community detection -- Dimensionality reduction. ;en_US
dc.format.extentXXXII, 857 p. 252 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10235. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10235. ;en_US
dc.relation.haspart9783319575292.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Securityen_US
dc.subjectDatabase Managementen_US
dc.subjectData Miningen_US
dc.subjectInformation Storage and Retrievalen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectData Mining and Knowledge Discoveryen_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.titleAdvances in Knowledge Discovery and Data Miningen_US
dc.title.alternative21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcQA76.9.D343 ;en_US
dc.classification.dc006.312 ; 23 ;en_US
Appears in Collections:مهندسی فناوری اطلاعات

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorKim, Jinho. ;en_US
dc.contributor.authorShim, Kyuseok. ;en_US
dc.contributor.authorCao, Longbing. ;en_US
dc.contributor.authorLee, Jae-Gil. ;en_US
dc.contributor.authorLin, Xuemin. ;en_US
dc.contributor.authorMoon, Yang-Sae. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:30:29Z-
dc.date.available2020-05-17T08:30:29Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319575292 ;en_US
dc.identifier.isbn9783319575285 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1630-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319575285. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction. ;en_US
dc.description.statementofresponsibilityedited by Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, Yang-Sae Moon.en_US
dc.description.tableofcontentsClassification and deep learning -- Social network and graph mining -- Privacy-preserving mining and security/risk applications -- Spatio-temporal and sequential data mining -- Clustering and anomaly detection -- Recommender system -- Feature selection -- Text and opinion mining -- Clustering and matrix factorization -- Dynamic, stream data mining -- Novel models and algorithms -- Behavioral data mining -- Graph clustering and community detection -- Dimensionality reduction. ;en_US
dc.format.extentXXXII, 857 p. 252 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10235. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10235. ;en_US
dc.relation.haspart9783319575292.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Securityen_US
dc.subjectDatabase Managementen_US
dc.subjectData Miningen_US
dc.subjectInformation Storage and Retrievalen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectData Mining and Knowledge Discoveryen_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.titleAdvances in Knowledge Discovery and Data Miningen_US
dc.title.alternative21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcQA76.9.D343 ;en_US
dc.classification.dc006.312 ; 23 ;en_US
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319575292.pdf46.28 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKim, Jinho. ;en_US
dc.contributor.authorShim, Kyuseok. ;en_US
dc.contributor.authorCao, Longbing. ;en_US
dc.contributor.authorLee, Jae-Gil. ;en_US
dc.contributor.authorLin, Xuemin. ;en_US
dc.contributor.authorMoon, Yang-Sae. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:30:29Z-
dc.date.available2020-05-17T08:30:29Z-
dc.date.issued2017en_US
dc.identifier.isbn9783319575292 ;en_US
dc.identifier.isbn9783319575285 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1630-
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319575285. ;en_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction. ;en_US
dc.description.statementofresponsibilityedited by Jinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, Yang-Sae Moon.en_US
dc.description.tableofcontentsClassification and deep learning -- Social network and graph mining -- Privacy-preserving mining and security/risk applications -- Spatio-temporal and sequential data mining -- Clustering and anomaly detection -- Recommender system -- Feature selection -- Text and opinion mining -- Clustering and matrix factorization -- Dynamic, stream data mining -- Novel models and algorithms -- Behavioral data mining -- Graph clustering and community detection -- Dimensionality reduction. ;en_US
dc.format.extentXXXII, 857 p. 252 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10235. ;en_US
dc.relation.ispartofseriesLecture Notes in Computer Science, ; 0302-9743 ; ; 10235. ;en_US
dc.relation.haspart9783319575292.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Securityen_US
dc.subjectDatabase Managementen_US
dc.subjectData Miningen_US
dc.subjectInformation Storage and Retrievalen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Scienceen_US
dc.subjectData Mining and Knowledge Discoveryen_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.titleAdvances in Knowledge Discovery and Data Miningen_US
dc.title.alternative21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II /en_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
dc.classification.lcQA76.9.D343 ;en_US
dc.classification.dc006.312 ; 23 ;en_US
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319575292.pdf46.28 MBAdobe PDFThumbnail
Preview File