Data Science: Foundations and Applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VII

Xintao Wu

ISBN 10: 9819682975 / ISBN 13: 9789819682973

Published by Springer Nature Singapore, Springer Nature Singapore, 2025

About this Item

Druck auf Anfrage Neuware - Printed after ordering - The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10 13, 2025.The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.

Seller Inventory # 9789819682973

Bibliographic Details

• Title: Data Science: Foundations and Applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VII
• Publisher: Springer Nature Singapore, Springer Nature Singapore
• Publication Date: 2025
• Language: English
• ISBN 10: 9819682975
• ISBN 13: 9789819682973
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 26D millimeters 235 millimeters width by 155 millimeters height by 26 millimeters depth
• Weight: 709 grams

About this title

Synopsis

The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025.

The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.