14th International Conference on Artificial Intelligence, Data Mining & Knowledge Management (AIDMK 2026)
June 27 ~ 28, 2026, Copenhagen, Denmark
Scope & Topics 14th International Conference on Artificial Intelligence, Data Mining & Knowledge Management (AIDMK 2026) serves as a premier global forum for presenting and discussing the latest innovations, research findings, and emerging trends across the rapidly evolving fields of AI driven data mining and knowledge management. As organizations increasingly rely on intelligent systems to extract insights, automate decision-making, and manage vast volumes of complex data, the need for advanced methodologies and deeper theoretical understanding has never been greater.
Authors are invited to contribute high quality research articles, innovative project results, comprehensive surveys, and real world industrial experiences that demonstrate significant advances in data mining, AI driven knowledge discovery, and related domains such as signal processing, image analysis, and pattern recognition. By providing a platform for sharing breakthroughs and practical insights, AIDMK 2026 seeks to advance the state of the art and inspire new directions in intelligent data and knowledge technologies. Topics of interest include, but are not limited to, the following Foundations of Data Mining, Machine Learning & AI
- Scalable, Parallel, and Distributed Data Mining Algorithms
- Mining Data Streams, Real Time Analytics, and Online Learning
- Graph Mining, Network Science, and Knowledge Graph Mining
- Spatial, Temporal, and Spatio Temporal Data Mining
- Text, Multimedia, Video, and Multimodal Data Mining
- Web Mining, Social Media Mining, and Behavioral Analytics
- Feature Engineering, Data Cleaning, and Pre Processing Pipelines
- Robust Learning from Noisy, Incomplete, or Low Quality Data
- Explainable AI (XAI), Interpretable Models, and Transparent Mining
- Privacy Preserving Data Mining (DP, MPC, Federated Analytics)
- Adversarial Machine Learning, Robustness, and Secure Data Mining
Advanced AI & Machine Learning for Knowledge Discovery
- Deep Learning Architectures for Knowledge Extraction
- Foundation Models, LLMs, and Multimodal AI for Data Mining
- Retrieval Augmented Generation (RAG) and LLM Integrated Knowledge Discovery
- Self -Supervised, Weakly Supervised, and Semi Supervised Learning
- Reinforcement Learning, Sequential Decision Making, and Policy Learning
- Causal Discovery, Causal Inference, and Counterfactual Reasoning
- AutoML, Neural Architecture Search, and Automated Knowledge Discovery
- Generative AI, Synthetic Data, and Data Augmentation
- Trustworthy AI: Fairness, Bias Mitigation, and Responsible Data Mining
Big Data Platforms, AI Systems & Scalability
- Distributed Data Processing Frameworks (Spark, Flink, Ray, etc.)
- High Performance Data Mining on Cloud, Edge, and Hybrid Systems
- AI Systems: Training, Serving, and Optimization for Large Models
- Data Mining on GPUs, TPUs, and Accelerator Rich Architectures
- Streaming Systems, Event Driven Pipelines, and Real Time Analytics
- Data Lakehouse Architectures and Large Scale Data Management
- Scalable ML Systems, MLOps, and End to End AI Pipelines
- Benchmarking, Performance Evaluation, and Optimization of AI/ML Systems
Knowledge Management, Reasoning & Representation
- Knowledge Representation, Ontologies, and Semantic Technologies
- Knowledge Graph Construction, Completion, Alignment, and Reasoning
- Integration of Data Warehousing, OLAP, and Modern Data Platforms
- Knowledge Discovery Frameworks, Pipelines, and Process Automation
- Pre and Post Processing for KDD: Summarization, Explanation, Validation
- Interactive Data Exploration, Visualization, and Human in the Loop Mining
- Languages, Interfaces, and Tools for Knowledge Management
- Mining and Reasoning over Foundation Model Outputs
- AI Driven Knowledge Management and Enterprise Intelligence
Applications of Data Mining, Knowledge Management & AI
- Bioinformatics, Genomics, and Computational Biology
- Healthcare Analytics, Medical AI, and Clinical Decision Support
- Financial Modeling, Fraud Detection, and Risk Analytics
- Cybersecurity Analytics and Threat Intelligence
- Image, Video, and Sensor Data Analysis
- Social Network Analysis and Community Detection
- Educational Data Mining and Learning Analytics
- Recommender Systems, Personalization, and User Modeling
- Smart Cities, IoT Analytics, and Cyber Physical Systems
- Environmental, Climate, and Sustainability Data Mining
- Scientific Discovery, Simulation Driven Analytics, and Digital Twins
- AI Enhanced Decision Support and Intelligent Automation
Emerging Trends & Future Directions in AI Driven Knowledge Discovery
- Federated Learning, Collaborative Mining, and Edge Intelligence
- Data Centric AI: Data Quality, Curation, Governance, and Lineage
- Responsible AI: Ethics, Transparency, and Compliance in Data Mining
- Temporal Knowledge Discovery, Event Forecasting, and Sequence Modeling
- Graph Neural Networks (GNNs), Graph Transformers, and Dynamic Graph Mining
- Mining Massive Knowledge Bases and Large Scale Foundation Models
- AI for Scientific Discovery and Research Acceleration
- Opportunities, Risks, and Societal Impact of AI Driven Data Mining
Paper Submission Authors are invited to submit papers through the conference Submission System by May 02, 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 AIDMK 2026, after further revisions, will be published in the special issue of the following journals. Important Dates | Submission Deadline | : | May 02, 2026 | | Authors Notification | : | May 23, 2026 | | Final Manuscript Due | : | May 30, 2026 |
Co - Located Event
***** The invited talk proposals can be submitted to dkmp@bdml2026.org
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