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

Website https://bdbs2026.org/dmml/index | Edit Freely

Category Data Mining Foundations; Data Streams Mining; Graph Mining

Deadline: March 07, 2026 | Date: April 25, 2026-April 26, 2026

Venue/Country: Copenhagen, Denmark, Denmark

Updated: 2026-03-02 16:18:09 (GMT+9)

Call For Papers - CFP

April 25 ~ 26, 2026, Copenhagen, Denmark

https://bdbs2026.org/dmml/index

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DMML 2026 welcomes original, unpublished work that pushes the boundaries of theory, algorithms, systems, and applications.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 include, but are not limited to, the following

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 March 07, 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.

• International Journal of Multimedia & Its Applications (IJMA) -

• International Journal of Data Mining & Knowledge Management Process (IJDKP)

• International Journal of Database Management Systems (IJDMS)- WJCI, Indexed

• Machine Learning and Applications: An International Journal (MLAIJ)

• International Journal of Web & Semantic Technology (IJWesT)

• Advances in Vision Computing: An International Journal (AVC)

Important Dates

2nd batch: Submissions after March 01

• Submission Deadline: March 07, 2026

• Authors Notification: April 04, 2026

• Registration & camera - Ready Paper Due: April 11, 2026

Contact Us

Here's where you can reach us: dmmlbdbs2026.org (or) dmmlconfeeyahoo.com

Paper Submission Link: https://necom2026.org/submission/index.php