Advances in Knowledge Discovery and Data Mining | 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part II

Xintao Wu (u. a.)

ISBN 10: 9819681723 / ISBN 13: 9789819681723

Published by Springer, 2025

About this Item

Advances in Knowledge Discovery and Data Mining | 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part II | Xintao Wu (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xxx | Englisch | 2025 | Springer | EAN 9789819681723 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

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Bibliographic Details

• Title: Advances in Knowledge Discovery and Data Mining | 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part II
• Publisher: Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 9819681723
• ISBN 13: 9789819681723
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 29D millimeters 235 millimeters width by 155 millimeters height by 29 millimeters depth
• Weight: 791 grams
• Seller catalogs:
  • Bücher

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Synopsis

The five-volume set, LNAI 158710 - 15874 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, held in Sydney, New South Wales, Australia, during June 10–13, 2025.

The conference received a total of 557 submissions to the main track, 35 submissions to the survey track and 104 submittion to the special track on LLMs. Of these, 134 papers have been accepted for the main track, 10 for the survey track and 24 for the LLM track. 68 papers have been transferred to the4 DSFA special session. 

The papers have been organized in topical sections as follows: 

Part I: Anomaly Detection; Business Data Analysis; Clustering; Continual Learning; Contrastive Learning; Data Processing for Learning; 

Part II: Fairness and Interpretability; Federated Learning; Graph Mining and GNN; Learning on Scientific Data; 

Part III: Machine Learning; Multi-modality; OOD and Optimization; Recommender Systems; Representation Learning and Generative AI; 

Part IV: Security and Privacy; Temporal Learning; Survey; 

Part V: LLM Fine-tuning and Prompt Engineering; Fairness and Interpretability of LLMs; LLM Application; OOD and Optimization of LLMs.