7th International Conference on Data Mining & Machine Learning (DMML 2026)

Program Committee

Accepted Papers

Copenhagen, Denmark

Scope

7^th International Conference on Data Mining & Machine Learning (DMML 2026) invites high quality research contributions from academia, industry, and government. As one of the leading global forums for presenting advances in data driven intelligence, DMML brings together researchers, practitioners, and innovators to exchange ideas, discuss emerging trends, and shape the future of data mining and machine learning.

Call for Papers

DMML 2026 welcomes original, unpublished work that pushes the boundaries of theory, algorithms, systems, and applications. Submissions will undergo a rigorous peer review process, and accepted papers will be included in the conference proceedings.

DMML 2026 covers a broad spectrum of topics in data mining, machine learning, and knowledge discovery. We particularly encourage submissions that address cutting edge challenges, propose novel methodologies, or demonstrate impactful real world applications.

Topics of Interest

Foundations of Data Mining & Machine Learning

Theoretical foundations of data mining
Statistical learning theory
Optimization methods for ML
Causality and causal discovery
Explainable and interpretable AI
Fairness, accountability, transparency, and ethics
Robust and trustworthy ML
Uncertainty modeling and noise handling

Algorithms & Models

Classification, regression, and clustering
Ensemble learning and hybrid models
Deep learning architectures (CNNs, RNNs, Transformers, GNNs)
Graph mining and graph ML
Reinforcement learning
Probabilistic and Bayesian models
Transfer learning, domain adaptation, multi task learning
Online learning and data stream mining
Federated and privacy preserving learning
Large scale and distributed data mining algorithms

Data Processing & Engineering

Data cleaning, transformation, and pre processing
Feature engineering and feature selection
Data integration, fusion, and warehousing
ETL pipelines for ML systems
High performance and parallel computing
Edge, cloud, and distributed ML systems
Efficient model training, compression, and deployment

Knowledge Discovery & Pattern Mining

Frequent pattern and sequential pattern mining
Anomaly, outlier, and novelty detection
Temporal, spatial, and spatio temporal mining
Mining from incomplete or low quality data
Knowledge representation and reasoning
Knowledge graphs and semantic mining
Automated knowledge consolidation and explanation

Text, Language & Multimedia Mining

Natural language processing and text mining
Large language models and foundation models
Information retrieval and web mining
Social media and social network analysis
Image, video, and audio mining
Multimodal learning and cross media analysis
Generative models (GANs, diffusion models, multimodal generators)

Visualization, Interaction & Human Centered AI

Interactive data exploration and visual analytics
Human AI collaboration and human in the loop ML
Interfaces and languages for data mining
Visualization of complex models and explanations
User centered evaluation of ML systems

Security, Privacy & Responsible AI

Privacy preserving data mining (DP, MPC, FL)
Adversarial machine learning
Data security and information hiding
ML safety and risk assessment
Ethical and societal implications of AI

Applications of Data Mining & Machine Learning

Bioinformatics, genomics, and computational biology
Biometrics and identity recognition
Healthcare and medical imaging
Finance, forecasting, and risk modeling
Education and learning analytics
Smart cities, IoT, and sensor data mining
Cybersecurity and fraud detection
E commerce and recommendation systems
Climate science and environmental modeling
Industrial AI and predictive maintenance

Emerging Topics & Future Directions

Foundation models and general purpose AI
Autonomous systems and robotics
Quantum machine learning
Neuro symbolic AI
ML for scientific discovery
AI governance, policy, and global standards
Trends, opportunities, and risks in data mining & ML

Paper Submission

Authors are invited to submit papers through the conference Submission System by February 01, 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 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 DMML 2026, after further revisions, will be published in the special issue of the following journals..

Important Dates

Submission Deadline

February 01, 2026

Authors Notification

March 14, 2026

Registration & camera - Ready Paper Due

March 21, 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 DMML 2026