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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 / De-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin, editors.

By: Contributor(s): Material type: TextTextSeries: Lecture notes in computer science. Lecture notes in artificial intelligence. | Lecture notes in computer science ; 14645. | LNCS sublibrary. SL 7, Artificial intelligence.Publisher: Singapore : Springer, 2024Description: 1 online resource (xxxiii, 380 pages) : illustrations (some color)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789819722426
  • 981972242X
Other title:
  • PAKDD 2024
Subject(s): DDC classification:
  • 006.3/12 23/eng/20240501
LOC classification:
  • QA76.9.D343
Online resources:
Contents:
Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References
Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction
2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work
2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models -- 2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation
3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References
Summary: 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.
Holdings
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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.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed May 1, 2024).

Intro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part I -- Anomaly and Outlier Detection -- Spatial-Temporal Transformer with Error-Restricted Variance Estimation for Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Problem Formulation -- 3.2 Proposed Architecture -- 3.3 Error-Restricted Probability (ERP) Loss -- 3.4 Anomaly Score -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Baseline Methods -- 4.3 Experimental Settings -- 4.4 Overall Results -- 4.5 Ablation Study -- 5 Conclusion -- References

Multi-task Contrastive Learning for Anomaly Detection on Attributed Networks -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Framework -- 3.1 Subgraph Sampling Based Data Augmentation -- 3.2 Context Matching Contrastive Learning -- 3.3 Link Prediction Contrastive Learning -- 3.4 Model Training and Anomaly Score Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Result and Analysis -- 4.3 Ablation Study -- 4.4 Parameter Study -- 5 Related Works -- 6 Conclusions -- References -- SATJiP: Spatial and Augmented Temporal Jigsaw Puzzles for Video Anomaly Detection -- 1 Introduction

2 Related Works -- 3 Problem Formulation: Frame-Level VAD -- 4 Proposal: SATJiP -- 4.1 Preliminary -- 4.2 Masked Temporal Jigsaw Puzzles (MTJiP) -- 5 Experiments -- 5.1 Datasets and Evaluation Metric -- 5.2 Implementation Details -- 5.3 Comparison in Detecting Accompanying Anomalies (AA) -- 5.4 Comparison in Detecting Diverse Video Anomalies -- 5.5 Ablation Study -- 5.6 VAD Examples -- 6 Conclusion -- References -- STL-ConvTransformer: Series Decomposition and Convolution-Infused Transformer Architecture in Multivariate Time Series Anomaly Detection -- 1 Introduction -- 2 Related Work

2.1 Prediction-Based Models -- 2.2 Reconstruction-Based Models -- 2.3 Transformers for Time Series Analysis -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Overall Architecture -- 3.3 Data Preprocessing -- 3.4 Decomposition Block -- 3.5 Local-Transformer Encoder and Decoder -- 3.6 Loss Function and Anomaly Score -- 4 Experiments -- 5 Conclusion -- References -- TOPOMA: Time-Series Orthogonal Projection Operator with Moving Average for Interpretable and Training-Free Anomaly Detection -- 1 Introduction -- 2 Related Work -- 3 TOPOMA: Our Proposed Anomaly Detector -- 3.1 Problem Formulation

3.2 Moving Average of Orthogonal Projection Operators -- 3.3 Adaptive Choice of Anomaly Score Thresholds -- 3.4 Complexity Analysis -- 4 Results and Discussion -- 4.1 Synthetic Data -- 4.2 Real-World Data -- 5 Conclusion -- References -- Latent Space Correlation-Aware Autoencoder for Anomaly Detection in Skewed Data -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 3.1 Robust Hybrid Error with MD in Latent Space -- 3.2 Objective Function -- 4 Experiments -- 4.1 Datasets -- 4.2 Baseline Methods -- 4.3 Ablation Study -- 5 Hyperparameter Sensitivity -- 6 Conclusion -- References

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