Advances in Knowledge Discovery and Data Mining (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
556
Utgivningsdatum
2019-03-20
Upplaga
1st ed. 2019
Förlag
Springer Nature Switzerland AG
Medarbetare
Yang, Qiang (ed.), Zhou, Zhi-Hua (ed.), Gong, Zhiguo (ed.), Zhang, Min-Ling (ed.), Huang, Sheng-Jun (ed.)
Illustratör/Fotograf
Bibliographie 180 schwarz-weiße Abbildungen
Illustrationer
159 Illustrations, color; 99 Illustrations, black and white; XXVIII, 556 p. 258 illus., 159 illus. i
Dimensioner
234 x 156 x 30 mm
Vikt
808 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030161415
Advances in Knowledge Discovery and Data Mining (häftad)

Advances in Knowledge Discovery and Data Mining

23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III

Häftad Engelska, 2019-03-20
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The three-volume set LNAI 11439, 11440, and 11441 constitutes the thoroughly refereed proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, held in Macau, China, in April 2019. The 137 full papers presented were carefully reviewed and selected from 542 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: classification and supervised learning; text and opinion mining; spatio-temporal and stream data mining; factor and tensor analysis; healthcare, bioinformatics and related topics; clustering and anomaly detection; deep learning models and applications; sequential pattern mining; weakly supervised learning; recommender system; social network and graph mining; data pre-processing and featureselection; representation learning and embedding; mining unstructured and semi-structured data; behavioral data mining; visual data mining; and knowledge graph and interpretable data mining.
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