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

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Link: https://bdbs2026.org/dmml/index
 
When Apr 25, 2026 - Apr 26, 2026
Where Copenhagen, Denmark
Submission Deadline Mar 14, 2026
Notification Due Apr 4, 2026
Final Version Due Apr 11, 2026
Categories    machine learning   artificial intelligence   computer science   data mining
 

Call For Papers

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

April 25 ~ 26, 2026, Copenhagen, Denmark

Hybrid -- Registered authors can present their work online or face to face.

Scope & Topics

7th International Conference on Data Mining & Machine Learning (DMML 2026) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Mining and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Data Mining and Machine Learning.

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 14, 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 (H index 45) in Computer Science & Information Technology (CS & IT) series (Confirmed).

Selected papers from DMML 2026, after further revisions, will be published in the special issues of the following journals.

Important Dates

Submission Deadline: March 14, 2026
Authors Notification: April 04, 2026
Final Manuscript Due: April 11, 2026

Co - Located Event


***** The invited talk proposals can be submitted to dmml@bdbs2026.org

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