IEEE International Conference on Data Mining (ICDM) — 2026
November 12-15, 2026
Northeastern University (NEU)Shenyang, China
Data Mining Machine Learning
Important Dates
| Submission deadline | June 6, 2026 (closed) |
| Notification of acceptance | August 16, 2026 |
ICDM is the world’s premier research conference in data mining, covering algorithms, systems, and applications across machine learning, deep learning, graph mining, time series analysis, and recommender systems. It has shaped the field for over two decades with a rigorous, selective review process and a program spanning both academic research and industry applications. Data scientists, researchers, and engineers attend to present novel methods and connect with the global data mining community.
Venue
Northeastern University (NEU)
No. 3-11 Wenhua Road, Heping District, Shenyang, Liaoning 110819, China
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AI-ready Data for Science
Focuses on preparing data for AI-driven scientific discovery.
AI-for-Science Benchmarking
Covers benchmarking AI methods for scientific applications.
Adaptable Reliable Responsible Learning
Focuses on adaptability, reliability, and responsibility in machine learning.
Foundation Models for Biology
Explores foundation models for biology and bioinnovation.
Data and Database Systems for AI
Covers data and database systems that underpin modern AI.
Deep Learning and Clustering
Focuses on deep-learning approaches to clustering and representation learning.
Data Mining for Ambient Intelligence
Applies data mining to ambient intelligence and secure communications.
Data Mining in Biomedical Informatics
Applies data mining to biomedical informatics and healthcare data.
Data Mining for Service
Applies data mining to service systems and service science.
Evolutionary Data Mining
Covers evolutionary computation approaches to data mining and machine learning.
Evolving Graph Data Mining
Focuses on mining evolving graphs and continual relational learning.
GNN-LLM Synergy
Combines graph neural networks with large language models for reasoning over structure.
Human Dynamics and Mobility Analytics
Focuses on mining human dynamics and mobility in the age of AI agents.
High Dimensional Data Mining
Focuses on mining and learning from high-dimensional data.
Incremental and Continual Learning
Covers incremental, continual, and lifelong learning methods.
Knowledge Discovery in ISAC
Applies knowledge discovery to integrated sensing and communication systems.
LLMs for Multimodal Data Fusion
Applies large language models to fusing multimodal data.
LLMs and Tensor Analysis
Explores the interplay of large language models and tensor analysis.
Large Models for Time Series
Covers large and foundation models applied to time series data mining.
Medical Time Series Foundation Models
Covers foundation models for medical time series analytics.
Mental Health on Social Media
Covers detecting mental-health signals in social media data.
Multimodal Mining for Agriculture
Applies multimodal data mining to sustainable agriculture.
Machine Learning for Cybersecurity
Applies machine learning to cybersecurity problems.
Optimization for Data Mining
Applies optimization techniques to emerging data mining problems.
Open World Anomaly Detection
Covers anomaly detection in open, dynamic, and evolving settings.
Pattern Mining for Bioinformatics
Applies pattern mining and machine learning to bioinformatics.
Clustering with Pretrained Models
Explores clustering using pretrained and foundation models.
Sentiment Analysis
Covers sentiment analysis and opinion mining from text.
Spatial and Spatiotemporal Data Mining
Covers data mining over spatial and spatiotemporal data.
Secure Federated Generative AI
Covers security and trust in federated generative AI.
Trustworthy ML for Decision-Making
Covers fairness, privacy, robustness, and explainability in decision-making ML.
Trustworthy Federated Learning for Industry
Focuses on trustworthy federated learning for smart industrial systems.
Trustworthy Recommender Systems
Focuses on trust, privacy, and unlearning in recommender systems.
Urban Intelligence and Mobility
Covers urban intelligence and multimodal mobility data mining.
AI for Personalization
Covers AI and data mining methods for personalization and recommendation.
World Models and Autonomous Intelligence
Explores world models and autonomous intelligence for data mining.
Our picks for IEEE International Conference on Data Mining (ICDM) attendees
A curated selection of hotels chosen for location, value, and fit for conference travellers.
DoubleTree by Hilton Shenyang
89 Fengyutan Street, Shenhe District, Shenyang 110013, China
Reliable Hilton property in Shenhe District convenient for the Northeastern University area.
Hilton Shenyang
374 Qingnian Street, Heping District, Shenyang 110004, China
Full-service Hilton in central Shenyang with indoor pool and spa, a short drive from the university campus.
Jinjiang Inn Shenyang Northeastern University
Wenhua Road, Shenhe District, Shenyang, China
Budget-friendly option closest to the Northeastern University campus.
NEU International Hotel Shenyang
80 West Wenti Road, Heping District, Shenyang 110004, China
On-campus hotel at Northeastern University - the most convenient option for ICDM conference attendees.
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