Lauw / Wong / Ntoulas | Advances in Knowledge Discovery and Data Mining | Buch | 978-3-030-47425-6 | www2.sack.de

Buch, Englisch, 886 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1369 g

Reihe: Lecture Notes in Computer Science

Lauw / Wong / Ntoulas

Advances in Knowledge Discovery and Data Mining

24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings, Part I
1. Auflage 2020
ISBN: 978-3-030-47425-6
Verlag: Springer

24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings, Part I

Buch, Englisch, 886 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1369 g

Reihe: Lecture Notes in Computer Science

ISBN: 978-3-030-47425-6
Verlag: Springer


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.

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Zielgruppe


Research

Weitere Infos & Material


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.



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