Advances in knowledge discovery and data mining : 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings
著者
書誌事項
Advances in knowledge discovery and data mining : 24th Pacific-Asia Conference, PAKDD 2020, Singapore, May 11-14, 2020, Proceedings
（Lecture notes in computer science, 12084-12085 . Lecture notes in artificial intelligence . LNCS sublibrary ; SL7 . Artificial Intelligence）
Springer, c2020
- pt. 1
- pt. 2
- タイトル別名
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PAKDD 2020
大学図書館所蔵 件 / 全2件
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注記
Other editors: Raymond Chi-Wing Wong, Alexandros Ntoulas, Ee-Peng Lim, See-Kiong Ng, Sinno Jialin Pan
Includes bibliographical references and index
内容説明・目次
- 巻冊次
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pt. 1 ISBN 9783030474256
内容説明
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.
目次
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.
- 巻冊次
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pt. 2 ISBN 9783030474355
内容説明
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.
目次
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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