12th International Conference on Data Mining (DaMi 2026)
July 25 ~ 26, 2026, Toronto, Canada Scope & Topics 12th International Conference on Data Mining (DaMi 2026) is a premier global forum dedicated to advancing the science, engineering, and practice of data mining and knowledge discovery. As data continues to grow in scale, complexity, and diversity, DaMi 2026 brings together researchers, practitioners, and industry innovators to explore the latest breakthroughs in machine learning, generative AI, large scale analytics, graph intelligence, multimodal mining, and responsible data driven systems.
DaMi 2026 embraces the rapidly evolving landscape of data centric research, including foundation models, vector databases, streaming analytics, federated learning, data centric AI, and human in the loop discovery. The conference aims to foster collaboration between academia and industry, highlight emerging trends, and provide a platform for presenting cutting edge research that shapes the future of intelligent data systems.
Authors are invited to contribute high quality research papers that present original results, innovative methodologies, practical applications, survey studies, or real world industrial experiences in all areas of data mining and knowledge discovery. DaMi 2026 welcomes contributions across a broad spectrum of topics, including (but not limited to):
Topics of interest include, but are not limited to, the following Foundations of Data Mining and Knowledge Discovery
- Theoretical Foundations of Data Mining
- Statistical Learning, Probabilistic Modeling and Bayesian Methods
- Pattern Discovery, Sequence Mining and Frequent Pattern Mining
- Causal Inference, Causal Discovery and Counterfactual Reasoning
- Robust Learning from Noisy, Incomplete and Low Quality Data
- Feature Engineering, Dimensionality Reduction and Representation Learning
- Post processing, Model Interpretation and Knowledge Explanation
- Data Centric AI Foundations and Data Quality Theory
Machine Learning, Deep Learning and Generative AI
- Supervised, Unsupervised and Semi Supervised Learning
- Deep Learning Architectures and Representation Learning
- Generative AI (GANs, Diffusion Models, Foundation Models)
- Retrieval Augmented Generation (RAG) and Knowledge Grounded Models
- Self Supervised and Contrastive Learning
- Transfer Learning, Domain Adaptation and Multi Task Learning
- Reinforcement Learning and Sequential Decision Making
- Large Scale ML Systems, Distributed Training and Model Parallelism
- Data Mining for LLM Training Pipelines and Dataset Curation
- Graph, Network and Structured Data Mining
- Graph Mining, Network Analysis and Link Prediction
- Graph Neural Networks (GNNs) and Graph Transformers
- Knowledge Graph Construction, Reasoning and Completion
- Temporal, Dynamic and Heterogeneous Graph Mining
- Graph Contrastive Learning and Graph Foundation Models
- Graph Based Anomaly Detection and Fraud Analytics
Multimodal, Text, Web and Social Data Mining
- Text Mining, NLP and LLM Driven Analytics
- Web Mining, Social Media Mining and Opinion/Sentiment Analysis
- Multimedia Mining (Image, Video, Audio, Multimodal Fusion)
- Multimodal Foundation Models (Vision Language, Audio Text, Video Text)
- Cross Modal Retrieval, Alignment and Multimodal RAG
- Spatio Temporal, Mobility and Geographical Data Mining
- Event Detection, Trend Analysis and Behavioral Modeling
Vector Databases, Embedding Based Retrieval and Semantic Search
- Vector Search and Approximate Nearest Neighbor (ANN)
- Embedding Based Retrieval and Indexing
- Semantic Search Pipelines and Hybrid Retrieval (Symbolic + Vector)
- Retrieval Optimization for LLMs and RAG Systems
- Large Scale Embedding Management and Drift Detection
Streaming, Real Time and Edge Data Mining
- Data Stream Mining and Online Learning
- Real Time Analytics and Low Latency Inference
- Edge Intelligence and On Device Data Mining
- Distributed Stream Processing (Flink, Spark Streaming, Ray)
- Adaptive Learning in Dynamic Environments
- Real Time Event Detection and Monitoring
Big Data, Cloud and Distributed Data Mining
- Scalable Data Mining Algorithms
- Parallel and Distributed Data Mining (Spark, Flink, Ray, Dask)
- Cloud Native Data Mining and Serverless Analytics
- Data Lakes, Lakehouses and Modern Data Engineering Pipelines
- GPU Accelerated Analytics and High Performance Data Mining
- Data Integration, Fusion and Multi Source Learning
- Data Lineage, Provenance and Versioning
Responsible AI, Fairness, Ethics and Governance
- Explainable AI (XAI) and Interpretable Models
- Fairness, Bias Detection and Algorithmic Accountability
- Ethical Data Mining and Responsible AI Practices
- Trustworthy AI, Safety and Risk Assessment
- Human Centered Data Mining and Decision Support
- AI Governance, Compliance and Regulatory Analytics
Privacy Preserving and Secure Data Mining
- Federated Learning and Collaborative Analytics
- Differential Privacy and Privacy Preserving Data Mining
- Secure Multi Party Computation and Homomorphic Encryption
- Adversarial Attacks, Robustness and Model Security
- Cybersecurity Analytics, Threat Detection and Anomaly Mining
- AI Safety Data Mining (jailbreak detection, harmful content detection)
Data Centric AI and Data Quality Engineering
- Data Quality, Cleaning, Labeling and Weak Supervision
- Data Validation, Error Detection and Data Debugging
- Data Centric AI Pipelines and Automated Data Preparation
- Data Valuation, Influence Functions and Data Attribution
- Synthetic Data Generation, Simulation and Evaluation
- Digital Twins for Data Driven Modeling
Interactive, Visual and Human in the Loop Data Mining
- Interactive Data Exploration and Visual Analytics
- Human in the Loop Learning and Collaborative Mining
- Visualization Techniques for Large Scale Data
- Interfaces, Tools and Languages for Data Mining
- Mixed Initiative Data Mining Systems
Knowledge Discovery Frameworks and Processes
- KDD Process Models, Workflow Automation and Pipelines
- Knowledge Representation, Reasoning and Ontologies
- Integration of Data Mining with Knowledge Graphs
- Evaluation Metrics, Benchmarking and Reproducibility
- Emerging Trends, Opportunities and Future Directions
Applications of Data Mining
- Bioinformatics, Computational Biology and Precision Medicine
- Financial Modeling, Fraud Detection and Risk Analytics
- Cybersecurity, Threat Intelligence and Intrusion Detection
- Healthcare Analytics and Medical Decision Support
- Educational Data Mining and Learning Analytics
- Smart Cities, IoT and Sensor Data Mining
- E commerce, Marketing, Recommendation Systems and Personalization
- Scientific Data Mining and Environmental Analytics
- Data Mining for Policy, Governance and Societal Impact
Paper Submission Authors are invited to submit papers through the conference Submission System by July 11, 2026 (Final Call). Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed). Selected papers from DaMi 2026, after further revisions, will be published in the special issue of the following journals. Important Dates | Submission Deadline | : | July 11, 2026 (Final Call) | | Authors Notification | : | July 18, 2026 | | Final Manuscript Due | : | July 22, 2026 |
Co - Located Event ***** The invited talk proposals can be submitted to dami@dami2026.org
|
