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Advances in Knowledge Discovery and Data Mining

28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Taipei, Taiwan, May 7–10, 2024, Proceedings, Part I

  • Conference proceedings
  • © 2024

Overview

Editors:
  1. De-Nian Yang

    1. Academia Sinica, Taipei, Taiwan

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  2. Xing Xie

    1. Microsoft Research Asia, Beijing, China

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  3. Vincent S. Tseng

    1. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

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  4. Jian Pei

    1. Duke University, Durham, USA

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  5. Jen-Wei Huang

    1. National Cheng Kung University, Tainan, Taiwan

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  6. Jerry Chun-Wei Lin

    1. Silesian University of Technology, Gliwice, Poland

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 14645)

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

Included in the following conference series:

Conference proceedings info: PAKDD 2024.

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About this book

The 6-volume set LNAI 14645-14650 constitutes the proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, which took place in Taipei, Taiwan, during May 7–10, 2024.

The 177 papers presented in these proceedings were carefully reviewed and selected from 720 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.

Keywords

Table of contents (29 papers)

  1. Front Matter

    Pages i-xxxiii
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  2. Anomaly and Outlier Detection

    1. Front Matter

      Pages 1-1
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    2. Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection

      • Yuye Feng, Wei Zhang, Haiming Sun, Weihao Jiang
      Pages 3-14

    3. Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks

      • Junjie Zhang, Yuxin Ding
      Pages 15-26

    4. SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection

      • Liheng Shen, Tetsu Matsukawa, Einoshin Suzuki
      Pages 27-40

    5. STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection

      • Yu-Xiang Wu, Bi-Ru Dai
      Pages 41-52

    6. TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection

      • Shanfeng Hu, Ying Huang
      Pages 53-65

    7. Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data

      • Padmaksha Roy, Himanshu Singhal, Timothy J O’Shea, Ming Jin
      Pages 66-77

    8. SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection

      • Phuong Nguyen, Tuan M. V. Le
      Pages 78-90

  3. Classification

    1. Front Matter

      Pages 91-91
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    2. QWalkVec: Node Embedding by Quantum Walk

      • Rei Sato, Shuichiro Haruta, Kazuhiro Saito, Mori Kurokawa
      Pages 93-104

    3. Human-Driven Active Verification for Efficient and Trustworthy Graph Classification

      • Tien-Cuong Bui, Wen-Syan Li
      Pages 105-116

    4. SASBO: Sparse Attack via Stochastic Binary Optimization

      • Yihan Meng, Weitao Li, Lin Shang
      Pages 117-129

    5. LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection

      • Kaiyue Wang, Fan Yang, Yucheng Yao, Xiabing Zhou
      Pages 130-142

    6. Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features

      • Xiang Ying, Shimei Luo, Mei Yu, Mankun Zhao, Jian Yu, Jiujiang Guo et al.
      Pages 143-155

    7. Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations

      • Daniel Shrewsbury, Suneung Kim, Young-Eun Kim, Heejo Kong, Seong-Whan Lee
      Pages 156-167

    8. Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification

      • Le Zhang, Raymond S. T. Lee
      Pages 168-180

    9. Adversarial Learning of Group and Individual Fair Representations

      • Hao Liu, Raymond Chi-Wing Wong
      Pages 181-193

    10. Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage

      • Jeremy Foxcroft, Peter Christen, Luiza Antonie
      Pages 194-205

  4. Clustering

    1. Front Matter

      Pages 207-207
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Other volumes

  1. Advances in Knowledge Discovery and Data Mining

  2. Advances in Knowledge Discovery and Data Mining

  3. Advances in Knowledge Discovery and Data Mining

  4. Advances in Knowledge Discovery and Data Mining

  5. Advances in Knowledge Discovery and Data Mining

  6. Advances in Knowledge Discovery and Data Mining

  7. Trends and Applications in Knowledge Discovery and Data Mining

Editors and Affiliations

  • Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  • Microsoft Research Asia, Beijing, China

    Xing Xie

  • National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  • Duke University, Durham, USA

    Jian Pei

  • National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  • Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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