Conference Information
PAKDD 2024: Pacific-Asia Conference on Knowledge Discovery and Data Mining
https://pakdd2024.org/Submission Date: |
2023-11-01 |
Notification Date: |
2024-01-10 |
Conference Date: |
2024-05-07 |
Location: |
Taipei, Taiwan |
Years: |
28 |
CCF: c CORE: a Viewed: 128043 Tracked: 305 Attend: 76
Call For Papers
Topics Topics of relevance for the conference include, but are not limited to, the following: Theoretical Foundations of Mining, Inference, and Learning Methods and Algorithms Association rule, classification, clustering, indexing, pattern mining, query analysis, and query processing Data/entity/event/relationship extraction, integration, cleaning, and summarization Anomaly and outlier detection, information retrieval and search, natural language processing, question answering, and recommendation systems Asymptotic analysis, intelligence analysis, online and streaming algorithms, and optimization methods Dimensionality detection and model selection, feature extraction and selection, kernel methods, matrices and tensors, neural network architectures, probabilistic models and statistical inference, and regression Deep and representation learning, reinforcement learning, relational/structured learning, semi-supervised learning, and unsupervised learning Few-shot learning, transfer learning, self-supervised learning, meta learning, interpretability, and explainability Measurements, evolution, and models of graphs and networks Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content Semi-structured data, text, web, and social media mining Spatio-temporal, time-series, sequential, and streaming data mining User activity and behavior data analysis, exploitation, and modeling Robustness, sampling, benchmarking, experiments, and evaluations Big Data Technologies Learning-based data cleaning and preparation Algorithmically efficient data transformation and integration and the trade-off between the complexity of the transformation and algorithmic efficiency Novel algorithmic and statistical techniques for scalable and high-performance big data mining Parallel and distributed data mining and searching on emerging architectures Analysis, sampling, evaluation, and search in big data and large-scale systems Collaborative search, sponsored search, voice search, conversational search, zero-query and implicit search, semantic search, and faceted search Human computation and crowdsourcing of big data User interfaces and visual analytics of big data analysis Security, Privacy, Ethics, Information Integrity, and Social Issues Modeling credibility, trustworthiness, and reliability Privacy-preserving data mining and privacy models Ethics of data mining, transparency, algorithmic bias, and fairness models Sources, origins, prevalence, and virality of misinformation, and monitoring and detection of misinformation Social issues, such as early childhood development, health inequities, mental health and well-being support systems, online education systems, and poverty Climate, ecological, and environmental science, and resilience and sustainability Interdisciplinary Research and Applications of Data Science Internet of Things, logistics management, network traffic and log analysis, and supply chain management Business and financial data, computational advertising, customer relationship management, intrusion and fraud detection, and intelligent assistants Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, and scientific data
Last updated by Dou Sun in 2023-09-02
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International Journal of Grid Computing & Applications | AIRCC | |
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