UID:
almahu_9948352076802882
Umfang:
XXXVII, 886 p. 293 illus., 207 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2020.
ISBN:
9783030474263
Serie:
Lecture Notes in Artificial Intelligence ; 12084
Inhalt:
The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 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: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.
Anmerkung:
Recommender Systems -- Classification -- Clustering -- Mining Social Networks -- Representation Learning and Embedding -- Mining Behavioral Data -- Deep Learning -- Feature Extraction and Selection -- Human, Domain, Organizational and Social Factors in Data Mining.
In:
Springer eBooks
Weitere Ausg.:
Printed edition: ISBN 9783030474256
Weitere Ausg.:
Printed edition: ISBN 9783030474270
Sprache:
Englisch
DOI:
10.1007/978-3-030-47426-3
URL:
https://doi.org/10.1007/978-3-030-47426-3
