15th International Conference on Data Mining & Knowledge Management Process (CDKP 2026)

15th International Conference on Data Mining & Knowledge Management Process (CDKP 2026)

June 20 ~ 21, 2026, Sydney, Australia

https://cdkp2026.org/index

Scope

The 15th International Conference on Data Mining & Knowledge Management Process (CDKP 2026) serves as a premier forum for researchers, practitioners, and industry experts to explore the evolving landscape of data driven discovery and intelligent knowledge management. As data continues to grow in scale, complexity, and strategic importance, the need for innovative methods to extract meaningful insights and support informed decision making has never been greater. CDKP 2026 brings together a global community of scholars to address these challenges and to share cutting edge advancements shaping the future of the field.


The conference provides a rigorous, peer reviewed platform for presenting original research, real world applications, case studies, and survey contributions that advance the state of the art in data mining and knowledge management. Authors are invited to submit high quality articles that highlight significant results, novel methodologies, emerging trends, and practical experiences across a wide range of related domains. Submissions may address, but are not limited to, the following areas:

Topics of interest

Foundations of Data Mining and Knowledge Discovery

• Theoretical Foundations of Data Mining
• Statistical Learning, Probabilistic Modeling and Causal Inference
• Pattern Discovery, Frequent Itemsets and Association Rules
• Data and Knowledge Representation
• Automated Knowledge Discovery Pipelines and Frameworks

Machine Learning, Deep Learning and Generative Models

• Supervised, Unsupervised and Semi Supervised Learning
• Deep Learning Architectures and Optimization
• Foundation Models and Generative AI (LLMs, Diffusion Models, Multimodal Models)
• Representation Learning, Embeddings and Metric Learning
• Transfer Learning, Domain Adaptation and Few Shot Learning

Big Data, Distributed and Scalable Analytics

• Distributed, Parallel and Cloud Native Data Mining
• Real Time Analytics and Data Stream Mining
• High Performance Data Mining Algorithms
• Federated Learning, Edge Analytics and Privacy Preserving ML
• Data Mining Systems, Infrastructure and GPU/TPU Acceleration

Text, Web, Social and Multimodal Mining

• Text Mining, NLP and Large Language Models
• Web Mining, Web Behavior Analytics and Clickstream Mining
• Social Network Analysis, Influence Modeling and Community Detection
• Multimedia Mining (Image, Video, Audio, Multimodal Fusion)
• Information Retrieval, Ranking and Search Based Mining

Graph, Spatial, Temporal and Complex Data Mining

• Graph Mining, Graph Neural Networks (GNNs) and Network Embeddings
• Spatial, Spatio Temporal and Mobility Data Mining
• Time Series Analysis, Forecasting and Anomaly Detection
• Mining Heterogeneous, High Dimensional and Low Quality Data
• IoT, Sensor and Cyber Physical Data Analytics

Data Quality, Preprocessing and Data Centric AI

• Data Cleaning, Noise Reduction and Missing Data Handling
• Feature Engineering, Feature Selection and Dimensionality Reduction
• Data Centric AI (data selection, augmentation, labeling, weak supervision)
• Data Integration, Warehousing, ETL and Lakehouse Architectures
• Interactive Data Exploration, Visualization and Human in the Loop Analytics

Explainability, Fairness, Ethics and Trustworthy Data Mining

• Explainable AI (XAI) and Interpretable Models
• Fairness, Bias Detection and Responsible Data Science
• Privacy Preserving Data Mining (Differential Privacy, Secure Computation)
• Adversarial Attacks, Robustness and Security in Data Mining
• Data Governance, Lineage, Compliance and Auditability

Knowledge Management, Reasoning and Decision Support

• Knowledge Processing, Reasoning and Inference
• Knowledge Graphs, Ontologies and Semantic Technologies
• Decision Support Systems and Predictive Analytics
• Consolidation, Evaluation and Explanation of Discovered Knowledge
• Human AI Collaboration in Data Analysis and Decision Making




Recommender Systems and Personalization

• Collaborative Filtering and Matrix Factorization
• Sequential, Context Aware and Deep Learning Based Recommendation
• LLM Enhanced Recommendation and Retrieval Augmented Systems
• Fairness, Robustness and Evaluation in Recommender Systems
• Applications in E commerce, Media, Education and Social Platforms

Synthetic Data, Simulation and Data Generation

• Synthetic Tabular, Time Series and Multimodal Data Generation
• Simulation Based Data Mining
• Privacy Preserving Synthetic Data
• Data Augmentation for ML and Data Mining
• Evaluation of Synthetic Data Quality

Applications of Data Mining

• Bioinformatics, Computational Biology and Healthcare Analytics
• Financial Modeling, Fraud Detection and Risk Analytics
• Educational Data Mining and Learning Analytics
• Marketing, E commerce and Customer Behavior Modeling
• Smart Cities, Transportation, Climate and Environmental Analytics

Paper Submission

Authors are invited to submit papers through the conference Submission System by June 13, 2026(Final Call). 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 46) in Computer Science & Information Technology (CS & IT) series (Confirmed).


Selected papers presented at CDKP 2026, following an additional round of revisions, will be considered for publication in special issues of the following journals

• International Journal of Database Management Systems (IJDMS)
• International Journal of Data Mining & Knowledge Management Process (IJDKP)
• International Journal of Grid Computing & Applications (IJGCA)
• Information Technology in Industry (ITII)




Important Dates

• Submission Deadline: June 13, 2026(Final Call)

• Authors Notification: June 18, 2026

• Registration & Camera-Ready Paper Due: June 19, 2026

Contact Us: [email protected]