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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 I

  • Conference proceedings
  • Jul 2025

Overview

Editors:
  1. Xintao Wu

    1. University of Arkansas, Fayetteville, USA

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  2. Myra Spiliopoulou

    1. Otto-von-Guericke University Magdeburg, Magdeburg, Germany

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  3. Can Wang

    1. Griffith University, Nathan, Australia

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  4. Vipin Kumar

    1. University of Minnesota, Minneapolis, USA

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  5. Longbing Cao

    1. Macquarie University, Macquarie Park, Australia

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  6. Yanqiu Wu

    1. Macquarie University, Macquarie Park, Australia

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  7. Yu Yao

    1. University of Sydney, Camperdown, Australia

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  8. Zhangkai Wu

    1. Macquarie University, Macquarie Park, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15870)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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About this book

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.

Keywords

  • machine learining
  • artificial intelligence
  • probability and statistic
  • security and privacy
  • Data mining
  • Knowledge discovery
  • Data science
  • Machine learning
  • Deep learning
  • Big data
  • Advanced analytics
  • Data analysis
  • Large language models
  • Survey
  • Data science foundations
  • Data mining applications
  • Privacy
  • Security
  • Decision-support systems
  • Visualization

Editors and Affiliations

  • University of Arkansas, Fayetteville, USA

    Xintao Wu

  • Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

  • Griffith University, Nathan, Australia

    Can Wang

  • University of Minnesota, Minneapolis, USA

    Vipin Kumar

  • Macquarie University, Macquarie Park, Australia

    Longbing Cao, Yanqiu Wu, Zhangkai Wu

  • University of Sydney, Camperdown, Australia

    Yu Yao

Bibliographic Information

  • Book Title: Advances in Knowledge Discovery and Data Mining

  • Book Subtitle: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10–13, 2025, Proceedings, Part I

  • Editors: Xintao Wu, Myra Spiliopoulou, Can Wang, Vipin Kumar, Longbing Cao, Yanqiu Wu, Yu Yao, … Zhangkai Wu

  • Series Title: Lecture Notes in Computer Science

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025

  • Softcover ISBN: 978-981-96-8169-3Due: 20 July 2025

  • eBook ISBN: 978-981-96-8170-9Due: 20 July 2025

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XXX, 458

  • Number of Illustrations: 129 b/w illustrations

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