Advances in Knowledge Discovery and Data Mining
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part I
Published on 27. May 2023
Book
Paperback/Softback
XVII, 559 pages
978-3-031-33373-6 (ISBN)
Description
The 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25-28, 2023.
The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with 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, big data technologies, and foundations.
The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with 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, big data technologies, and foundations.
More details
Series
Edition
2023 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
132 farbige Abbildungen, 24 s/w Abbildungen
XVII, 559 p. 156 illus., 132 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 32 mm
Weight
867 gr
ISBN-13
978-3-031-33373-6 (9783031333736)
DOI
10.1007/978-3-031-33374-3
Schweitzer Classification
Other editions
Additional editions
Hisashi Kashima | Tsuyoshi Ide | Wen-Chih Peng
Advances in Knowledge Discovery and Data Mining
27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part I
E-Book
05/2023
Springer
€85.59
Available for download