Conference Information

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

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Submission Date:
2026-11-20 Due in 55 days
Notification Date:
2027-02-26
Conference Date:
2027-06-29
Location:
Wellington, New Zealand
Years:
CCF: C   ICORE: B   Viewed: 419287   Tracked: 312   Attend: 78

Conference Partner Index (CP-I)

72.3 / 100
Ranked #299 of 5,687 conferences · Top 6%

#16 of 337 in Data Mining & Databases

Academic recognition (35%)
72
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
93
Community attention (10%)
79
Public record completeness (15%)
70

Inputs used: Listed as CCF C, ICORE B · Editions on record: 31 · Researchers following it here: 312 · Researchers who opened this page in the past 24 months: 24

Missing from the public record: Historical acceptance rates (+4.5)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 80% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-26

Call For Papers

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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Best Papers

YearBest Papers
2019Parameter Transfer Unit for Deep Neural Networks
2019Towards one reusable model for various software defect mining tasks
2019Time-dependent Survival Neural Network for Remaining Useful Life Prediction
2018A Privacy Preserving Bayesian Optimization with High Efficiency
2018Cross-Domain Sentiment Classification via A Bifurcated-LSTM
2018Distributed representation of multi-sense words: A loss driven approach
2017Convolutional Bi-Directional LSTM for Detecting Inappropriate Query Suggestions in Web Search
2017Stable Bayesian Optimization
2017Energy-Based Localized Anomaly Detection in Video Surveillance
2016Frequent Pattern Outlier Detection without Exhaustive Mining
2016Effective Local Metric Learning for Water Pipe Assessment
2016Modeling Adversarial Learning as Nested Stackelberg Games
2015Stabilizing Sparse Cox Model Using Statistic and Semantic Structures in Electronic Medical Records
2015Context-Aware Detection of Sneaky Vandalism on Wikipedia across Multiple Languages
2015Maximizing Friend-Making Likelihood for Social Activity Organization
2015Collaborating Differently on Different Topics: A Multi-Relational Approach to Multi-Task Learning
2014Com2: Fast Automatic Discovery of Temporal (’Comet’) Communities
2014Inferring Metapopulation Based Disease Transmission Networks
2014Deferentially Private Tagging Recommendation based on Topic Model
2014Shifting Hypergraphs by Probabilistic Voting
2014Mining Contrast Subspaces
2013Matrix Factorization with Aggregated Observations
2013On Linear Refinement of Differential Privacy-Preserving Query Answering
2013ProCF: Generalising Probabilistic Collaborative Filtering for Reciprocal Recommendation
2013Efficient Mining of Contrast Patterns on Large Scale Imbalanced Real-life Data
2013One-Class Transfer Learning with Uncertain Data
2013Fast Graph Stream Classification Using Discriminative Clique Hashing
2012Generating Balanced Classifier-Independent Training Samples from Unlabeled Data
2012Detecting Multiple Stochastic Network Motifs in Network Data
2012Two-View Online Learning
2011The Role of Hubness in Clustering High-Dimensional Data
2011Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation
2011Constrained LDA for Grouping Product Features in Opinion Mining
2010oddball: Spotting Anomalies in Weighted Graphs
2010Supervising Latent Topic Model for Maximum-Margin Text Classification and Regression
2010Resource-bounded Information Extraction: Acquiring Missing Feature Values On Demand
2010A Novel Prototype Reduction Method for the K-Nearest Neighbor Algorithm with K>=1
2009Clustering with Lower Bound on Similarity
2009Interval Data Classification under Partial Information: A Chance-Constraint Approach
2009Detecting Abnormal Events via Hierarchical Dirichlet Processes
2009On Link Privacy in Randomizing Social Networks
2008A Framework for Modeling Positive Class Expansion with Single Snapshot
2008An Efficient Algorithm for Finding Similar Short Substrings from Large Scale String Data
2008Feature Selection by Nonparametric Bayes Error Minimization
2006Extracting and Summarizing Hot Item Features Across Different Auction Web
2006Variable Randomness in Decision Tree Ensembles
2004AutoSplit: Fast and Scalable Discovery of Hidden Variables in Stream and Multimedia Databases

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