Advances in Knowledge Discovery and Data Mining

Name: 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
Brand: Springer
Price: 139.09 EUR
Availability: OnlineOnly

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(Herausgeber*in)

Springer (Verlag)

Erschienen am 24. April 2024

XXXIII, 380 Seiten

E-Book

PDF mit Wasserzeichen-DRM

Systemvoraussetzungen

978-981-97-2242-6 (ISBN)

139,09 €inkl. 7% MwSt.

Systemvoraussetzungen

für PDF mit Wasserzeichen-DRM

E-Book Einzellizenz

Als Download verfügbar

Beschreibung

Weitere Details

Weitere Ausgaben

Inhalt

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
SeeM: A Shared Latent Variable Model for Unsupervised Multi-view Anomaly Detection
1 Introduction
2 Proposed Model for Multi-view Anomaly Detection
2.1 The SeeM Model and Its Inference
2.2 Complexity Analysis
2.3 Anomaly Score
3 Experiments
3.1 Datasets and Baselines
3.2 Multi-view Anomaly Detection Performance
3.3 Latent Dimension Analysis
3.4 Non-linear Projections
3.5 A Use Case with Real-World Multi-view Data
4 Related Work
5 Conclusion
References
Classification
QWalkVec: Node Embedding by Quantum Walk
1 Introduction
2 Preliminaries
2.1 Notations
2.2 Quantum Walks on Graphs
3 Related Works
3.1 Problems
4 Proposed Method: QWalkVec
4.1 Algorithm
5 Evaluations
5.1 Experimental Settings and Dataset
5.2 Overall Results
6 Conclusion
References
Human-Driven Active Verification for Efficient and Trustworthy Graph Classification
1 Introduction
2 Related Work
2.1 Human-in-the-loop Machine Learning
2.2 Deep Learning for Case-Based Reasoning
2.3 Interpretable Graph Neural Networks
3 Methodology
3.1 Problem Formulation and Framework Overview
3.2 Human-Compatible Representation Learning
3.3 Interpretable Predictor
3.4 Prediction Explanation
4 Experiments
4.1 Datasets and Baselines
4.2 Implementations and Configurations
4.3 Predictive Performance Comparison
4.4 Benefits of Human-AI Interactions
4.5 User Perception of Prediction Explanations
4.6 Is Instance-Level Feedback Helpful in Any Cases?
5 Discussions of Fairness and Ethical Issues
6 Conclusion and Future Work
References
SASBO: Sparse Attack via Stochastic Binary Optimization
1 Introduction
2 Related Work
3 Methods
3.1 Problem Definition
3.2 Sparse Adversarial Attack via Stochastic Binary Optimization
4 Experiments and Results
4.1 Non-targeted Attack
4.2 Targeted Attack
4.3 Visualization
5 Conclusion
References
LEMT: A Label Enhanced Multi-task Learning Framework for Malevolent Dialogue Response Detection
1 Introduction
2 Related Work
3 Method
3.1 Problem Definition
3.2 Overall Framework
3.3 Utterance Encoder
3.4 Malevolence Shift Detection
3.5 Hierarchy-Aware Label Encoder
3.6 Malevolence Detection in Dialogues
3.7 Multi-task Learning
4 Experiments
4.1 Datasets and Evaluation Metrics
4.2 Compared Baselines
4.3 Implementation Details
4.4 Main Results
4.5 Ablation Study
4.6 Analysis of Malevolence Shift Detection
4.7 Case Study
4.8 Analysis of LLMs
5 Conclusion
References
Two-Stage Knowledge Graph Completion Based on Semantic Features and High-Order Structural Features
1 Introduction
2 Preliminary
2.1 Knowledge Graph
2.2 Knowledge Graph Completion
2.3 Dynamic Graph Attention Variant GATv2
3 Methodology
3.1 Structural Local Contexts Aggregation
3.2 High-Order Connected Contexts Aggregation
3.3 Decoder
4 Experiment
4.1 Datasets and Metrics
4.2 Results and Analysis
4.3 Ablation Study
5 Conclusions
References
Instance-Ambiguity Weighting for Multi-label Recognition with Limited Annotations
1 Introduction
2 Related Works
2.1 Multi-label Recognition with Full Annotations
2.2 Multi-label Recognition with Limited Annotations
3 Methodology
3.1 Problem Definition
3.2 Ambiguity-Aware Instance Weighting
3.3 Total Training Loss
4 Experiments
4.1 Experiment Settings
4.2 Results
4.3 Ablation Studies
4.4 Model Analysis
5 Conclusion
References
Chaotic Neural Oscillators with Deep Graph Neural Network for Node Classification
1 Introduction
1.1 Node Classification
1.2 Graph Neural Network (GNN)
1.3 Chaotic Neural Oscillator (CNO)
2 Methodology
3 Experiment
3.1 Datasets
3.2 Settings and Baselines
3.3 Results
3.4 Ablation Study
4 Conclusion
References
Adversarial Learning of Group and Individual Fair Representations
1 Introduction
2 Related Work
3 Preliminaries
4 Methodology
4.1 Problem Statement
4.2 Model
4.3 Theoretical Properties of Loss Functions
4.4 Optimization with Focal Loss
5 Experiments and Analysis
6 Conclusion
References
Class Ratio and Its Implications for Reproducibility and Performance in Record Linkage
1 Introduction
2 Methodology
2.1 Data Partitioning
2.2 Classification and Evaluation
3 Experimental Study
3.1 Datasets
3.2 Results
4 Discussion and Recommendations
5 Conclusions and Future Work
References
Clustering
Clustering-Friendly Representation Learning for Enhancing Salient Features
1 Introduction
2 Related Works
3 Proposed Method
3.1 The Framework of cIDFD
3.2 Loss for Background Feature Extraction
3.3 Loss for Target Feature Extraction
3.4 Two-Stage Learning
4 Experiments
4.1 Datasets
4.2 Comparison with Conventional Methods
4.3 Representation Distribution
4.4 Similarity Distribution
5 Conclusion
References
ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning
1 Introduction
1.1 Motivation
1.2 Contribution
2 Related Work
3 Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning (ImMC-CSFL)
3.1 Deep Feature Extraction Module
3.2 Common Information Learning Module
3.3 Specific Information Learning Module
3.4 Deep Multi-view Clustering Based on Common-Specific Feature Learning
4 Experiment
4.1 Experimental Datasets and Evaluation Criteria
4.2 Methods of Comparison
4.3 Experimental Results
5 Summary
References
Multivariate Beta Mixture Model: Probabilistic Clustering with Flexible Cluster Shapes
1 Introduction
2 Multivariate Beta Mixture Model
2.1 Multivariate Beta Distribution
2.2 MBMM Density Function and Generative Process
2.3 Parameter Learning for the MBMM
2.4 The Similarity Score Between Data Points
3 Experiments
3.1 Comparisons on the Synthetic Datasets
3.2 Comparison on the Real Datasets
3.3 Distance Between Data Points
4 Related Work
5 Discussion
References
AutoClues: Exploring Clustering Pipelines via AutoML and Diversification
1 Introduction
2 Related Works
3 AutoClues
3.1 Formalization
3.2 Implementation
4 Benchmark Generation and Empirical Evaluation
4.1 Benchmark Generation
4.2 Effectiveness and Efficiency
4.3 Comparison
5 Conclusion and Future Work
References
Local Subsequence-Based Distribution for Time Series Clustering
1 Introduction
2 Related Work
3 Rd Domain Treatment: A Series Is Treated as a Set of Sliding-Window Points
4 Method
4.1 Isolation Distributional Kernel
4.2 Time Series Clustering Based on Distributional Kernel
5 Experiments
5.1 Experimental Results
5.2 Parameter Sensitivity Analysis
5.3 Visualization
6 Conclusion
References
Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model
1 Introduction
2 Related Work
3 Bayesian Non-parametric Latent Block Model
3.1 Model Definition
3.2 Inference
4 Proposed Inference
4.1 Worker Level
4.2 Master Level
5 Experiments
5.1 Experiment Settings
5.2 Clustering Performance
5.3 Comparison of the Distributed and Centralized Approaches
5.4 Distributed Algorithm Scalability
6 Conclusion
References
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
1 Introduction
2 Background and Related Works
3 The iFairNMTF Model
3.1 The iFairNMTF Model Formulation
3.2 The iFairNMTF Model Optimization
4 Experimental Evaluation
4.1 Experimental Setup
4.2 Clustering Quality vs Fairness
4.3 Parameter Analysis
4.4 Interpretability Analysis
5 Conclusion and Outlook
References
Data Mining Processes and Pipelines
NETEFFECT: Discovery and Exploitation of Generalized Network Effects
1 Introduction
2 Background and Related Work
2.1 Background
2.2 Related Work
3 Proposed GNE Test
3.1 NetEffect_Test
3.2 Discoveries on Real-World Graphs
4 Proposed GNE Estimation
4.1 Why NOT Edge Counting
4.2 Closed-Form Formula
4.3 NetEffect_Est
5 Proposed GNE Exploitation
5.1 ``Emphasis'' Matrix
5.2 NetEffect_Exp
6 Experiments
6.1 Q1 - Accuracy
6.2 Q2 - Scalability
6.3 Q3 - Explainability
7 Conclusions
References
Learning to Rank Based on Choquet Integral: Application to Association Rules
1 Introduction
2 Preliminaries
3 Learning a Choquet Ranking Function
4 Experiments
5 Conclusions
References
Saliency-Aware Time Series Anomaly Detection for Space Applications
1 Introduction
2 Background and Related Work
3 Methodology
4 Experiments
5 Results
6 Conclusion
References
A Model for Retrieving High-Utility Itemsets with Complementary and Substitute Goods
1 Introduction
2 Related Work
3 Model of High-Utility Itemsets with Substitute Items
4 Description of HUI Retrieval Scheme
5 Performance Evaluation
6 Conclusion
References
LPSD: Low-Rank Plus Sparse Decomposition for Highly Compressed CNN Models
1 Introduction
2 Related Work
3 LPSD Method
3.1 Mathematical Formulation
3.2 Alternative Optimization Method
3.3 Local Search Step (MinimizeError)
3.4 Complexity Analysis
4 Experiments
4.1 Experimental Setting
4.2 Experimental Results for ResNet20 Model on CIFAR-10
4.3 Experimental Results for ResNet50 Model on CIFAR-100
4.4 Experimental Results for ResNet50 Model on ImageNet
5 Conclusion and Future Work
References
Modeling Treatment Effect with Cross-Domain Data
1 Introduction
2 Preliminaries
3 Methodology
3.1 Collaborative and Adversarial Generalization
3.2 Pattern Augmentation
3.3 Further Extension and Discussion
4 Experiments
4.1 Datasets
4.2 Setup
4.3 Experimental Results and Analysis
4.4 Online Application (RQ4)
5 Conclusion
References
Author Index

Systemvoraussetzungen

Als PDF speichern Als Link merken