Advances in Knowledge Discovery and Data Mining (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
841
Utgivningsdatum
2017-04-23
Upplaga
1st ed. 2017
Förlag
Springer International Publishing AG
Medarbetare
Kim, Jinho (ed.), Shim, Kyuseok (ed.), Cao, Longbing (ed.), Lee, Jae-Gil (ed.), Lin, Xuemin (ed.), Moon, Yang-Sae (ed.)
Illustratör/Fotograf
Bibliographie
Illustrationer
242 Illustrations, black and white; XXXII, 841 p. 242 illus.
Volymtitel
Part I
Dimensioner
234 x 156 x 44 mm
Vikt
1203 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783319574530
Advances in Knowledge Discovery and Data Mining (häftad)

Advances in Knowledge Discovery and Data Mining

21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I

Häftad Engelska, 2017-04-23
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This 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.
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Innehållsförteckning

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.