12th International Conference on Data Mining (DaMi 2026)

Program Committee

Accepted Papers

Toronto, Canada

Scope

The 12^th 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.

Call for Papers

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 June 13, 2026. 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 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 journal.

Important Dates

Second Batch : Submissions after May 11, 2026

Submission Deadline

June 13, 2026

Authors Notification

July 12, 2026

Registration & camera - Ready Paper Due

July 18, 2026

Other Conferences

Past Events

Invited Talk

Proceedings

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC Digital Library

Welcome to DaMi 2026