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Advanced Data Mining and Applications

20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part II

  • Conference proceedings
  • © 2025

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Editors:
  1. Quan Z. Sheng

    1. Macquarie University, Sydney, Australia

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  2. Gill Dobbie

    1. University of Auckland, Auckland, New Zealand

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  3. Jing Jiang

    1. Australian National University, Canberra, Australia

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  4. Xuyun Zhang

    1. Macquarie University, Sydney, Australia

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  5. Wei Emma Zhang

    1. The University of Adelaide, Adelaide, Australia

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  6. Yannis Manolopoulos

    1. Open University of Cyprus, Nicosia, Cyprus

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  7. Jia Wu

    1. Macquarie University, Sydney, Australia

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  8. Wathiq Mansoor

    1. University of Dubai, Dubai, United Arab Emirates

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  9. Congbo Ma

    1. Macquarie University, Sydney, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 15388)

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

Included in the following conference series:

Conference proceedings info: ADMA 2024.

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About this book

This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024.

The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: -

Part I : Applications; Data mining.

Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph.

Part III : Graph mining; Spatial data mining.

Part IV : Health informatics.

Part V : Multi-modal; Natural language processing.

Part VI : Recommendation systems; Security and privacy issues.

 

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Table of contents (27 papers)

  1. Front Matter

    Pages i-xix
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  2. Data Mining Foundations and Algorithms

    1. Front Matter

      Pages 1-1
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    2. Evaluating Model Robustness Using Adaptive Sparse L0 Regularization

      • Weiyou Liu, Zhengyang Li, Weitong Chen
      Pages 3-17

    3. Expert-Guided Model Cultivation: CoTeaching to Resolve Abstruseness and Enhance Learning Performance

      • Siqi Zhang, Feng Zhou, Zhidong Li, Yang Wang, Donglian Qi, Shuming Li
      Pages 18-32

    4. DEKT: Difficulty Representation Enriched Knowledge Tracing

      • Rui Wang, Zhilong Shan
      Pages 33-47

    5. An Algorithm for Discovering Prevalent Co-location Patterns with Considering Both Density and Connectivity

      • Dinhsontung Ta, Phan Ha, Vanha Tran, Vanhieu Bui
      Pages 48-63

    6. An Ensemble Learning Method Based on Neighborhood Random Super-Reduct for Software Defect Number Prediction

      • Yuqi Sha, Yixuan Jiang, Junwei Du, Qiang Hu, Feng Jiang
      Pages 64-80

    7. A Cluster-Based Approach to kNN Join Over Batch-Dynamic High-Dimensional Data

      • Nimish Ukey, Guangjian Zhang, Zhengyi Yang, Xiaoyang Wang, Binghao Li, Serkan Saydam et al.
      Pages 81-96

    8. Multi-dimensional Classification via Global and Local Label Correlation

      • Zan Zhang, Jialin Zhou
      Pages 97-111

    9. An Intrusion Detection Model Based on CNN-BiLSTM

      • Liang Zhang, Jiatian Zhang
      Pages 112-126

    10. TBRL: Trajectory-Based Reinforcement Learning for Flexible Job-Shop Scheduling Problem

      • Zheng Chen, Ruijin Wang, Ye Zhu, Chao Tang, Donglin He, Jiachen Wang et al.
      Pages 127-142

    11. An Active Learning Method via Expected Model Loss Reduction

      • Yahe Li, Zhipeng Xie
      Pages 143-159

    12. When Molecular GAN Meets Byte-Pair Encoding

      • Huidong Tang, Chen Li, Yasuhiko Morimoto
      Pages 160-174

  3. Federated Learning

    1. Front Matter

      Pages 175-175
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    2. Efficient Clustered Federated Learning by Locality Sensitive Hashing

      • Lishan Yang, Alireza Seyed Shakeri, Liangxi Pu, Weitong Chen, Yanjun Shu
      Pages 177-191

    3. A Heterogeneous Federated Learning Method Based on Dual Teachers Knowledge Distillation

      • Siyuan Wu, Hao Tian, Weiran Zhang, Tingtong Zhu, Fuwen Tian, Zhehong Wang et al.
      Pages 192-207

    4. Towards Efficient Decentralized Federated Learning: A Survey

      • Saqr Khalil Saeed Thabet, Behnaz Soltani, Yipeng Zhou, Quan Z. Sheng, Shiting Wen
      Pages 208-222

    5. FedCLF – Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks

      • Kasun Eranda Wijethilake, Adnan Mahmood, Quan Z. Sheng
      Pages 223-238

    6. UFL: Unlinkable Federated Learning Through Shuffle and Shamir’s Secret Sharing

      • Jingxue Chen, Zhiwei Si, Jingcheng Song, Manoranjan Mohanty, Weiqi Wang, Hu Xiong
      Pages 239-253

    7. FairEquityFL – A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks

      • Fahmida Islam, Adnan Mahmood, Noorain Mukhtiar, Kasun Eranda Wijethilake, Quan Z. Sheng
      Pages 254-269
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Other volumes

  1. Advanced Data Mining and Applications

  2. Advanced Data Mining and Applications

  3. Advanced Data Mining and Applications

  4. Advanced Data Mining and Applications

  5. Advanced Data Mining and Applications

  6. Advanced Data Mining and Applications

Editors and Affiliations

  • Macquarie University, Sydney, Australia

    Quan Z. Sheng, Xuyun Zhang, Jia Wu, Congbo Ma

  • University of Auckland, Auckland, New Zealand

    Gill Dobbie

  • Australian National University, Canberra, Australia

    Jing Jiang

  • The University of Adelaide, Adelaide, Australia

    Wei Emma Zhang

  • Open University of Cyprus, Nicosia, Cyprus

    Yannis Manolopoulos

  • University of Dubai, Dubai, United Arab Emirates

    Wathiq Mansoor

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