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PAKDD 2027: Pacific-Asia Conference on Knowledge Discovery and Data Mining

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截稿日期:
2026-11-20 还有 56 天
通知日期:
2027-02-26
会议日期:
2027-06-29
会议地点:
Wellington, New Zealand
届数:
CCF: C   ICORE: B   浏览: 419154   关注: 312   参加: 78

会伴指数 (CP-I)

72.2 / 100
全站第 299 名 / 共 5,684 个会议 · 前 6%

数据挖掘与数据库 第 16 / 337

学术认可 (35%)
72
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
93
社区关注 (10%)
79
资料公开度 (15%)
70

用到的输入: 收录等级：CCF C, ICORE B · 有据可查的届次：31 · 在会伴关注它的研究者：312 人 · 过去 24 个月打开过本页的研究者：22 人

公开资料里还缺: 历年录用率 (+4.5)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-24

征稿

PAKDD 2027 (Pacific-Asia Conference on Knowledge Discovery and Data Mining) is a CCF C / ICORE B conference held in Wellington, New Zealand on 2027-06-29. The paper submission deadline is 2026-11-20. Acceptance notifications are sent on 2027-02-26.

PAKDD 2027 will be held exclusively in person. All accepted presentations must be delivered on-site. The 31st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) will take place in Wellington, New Zealand, on 29 June – 2 July 2027. PAKDD 2027 is soliciting contributed technical papers for presentation at the Conference and publication in the Conference Proceedings by Springer. We solicit novel, high-quality, and original research papers that provide innovative insights into all facets of knowledge discovery and data science, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, and integrity. We also encourage visionary papers on emerging topics and application-based papers offering innovative technical advancements to interdisciplinary research and applications of data science. Papers must be formatted using the Springer LNCS template and will be reviewed double-blind. The page limit and the submission site are still being confirmed and will be published here once they are settled. Topics of interest Topics of relevance for the conference include, but are not limited to, the following. Theoretical Foundations Mathematical, statistical, and information-theoretic foundations Optimization methods for data mining and machine learning Causal learning and causal inference Representation learning Non-IID learning and distribution shift Domain adaptation and domain generalisation Generalisation and out-of-distribution learning Neuro-symbolic learning and reasoning Generative modelling Quantum machine learning Foundations of trustworthy and responsible machine learning Learning Methods and Algorithms Clustering, classification, pattern mining and association rules discovery Supervised learning, semi-supervised learning, few-shot and zero-shot learning, active learning Reinforcement learning and bandits Transfer learning, federated learning Anomaly detection, outlier detection Learning in recommendation engines Learning in streams and in time series Learning on structured data, images, texts and multi-modal data Online learning, model adaption Graph mining and Graph NNs Trustworthy Machine Learning Fairness Data Processing for Learning Dimensionality reduction, feature extraction, subspace construction Data cleaning and preparation, data integration and summarization Learning in real-time Big data technologies Information retrieval Data/entity/event/relationship extraction User interfaces and visual analytics Security, Privacy, Ethics, Information Integrity and Social Issues Modeling credibility, trustworthiness, and reliability Privacy-preserving data mining and privacy models Model transparency, interpretability, and fairness Misinformation detection, monitoring, and prevention Social issues, such as health inequities, social development, and poverty Interdisciplinary Research on Data Science Applications Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection Symbiotic human-AI interaction, human-agent collaboration, socially interactive robots, and affective computing 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 Urban computing, spatial data science and pervasive computing Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content Climate, ecological, and environmental science, and resilience and sustainability Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, AI-assisted programming, and scientific data Other tracks Papers with a strong applied or industrial focus are better suited to the Applied Data Science Track, whose call will be published separately. Work on large language models and agentic AI for data science is better suited to the Special Track on Large Language Models and Agentic AI, whose scope has been extended from large language models to large language models and agentic AI.
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