Scope & Topics
The International Journal of Data Mining & Knowledge Management Process (IJDKP) is an open‑access, peer‑reviewed forum dedicated to advancing these rapidly evolving fields. The journal provides a platform for researchers and practitioners to present cutting‑edge developments in data mining, machine learning, knowledge discovery, and intelligent data processing. IJDKP welcomes contributions that explore foundational methods, innovative applications, and emerging technologies that push the boundaries of how knowledge is extracted, represented, and utilized.
Authors are invited to submit original research articles, technical reports, survey papers, and industrial case studies that demonstrate significant advances in data mining and knowledge management. Submissions may address any aspect of the discipline, including, but not limited to, the topics outlined in the journal’s scope. IJDKP aims to foster collaboration between researchers and industry professionals, promote the exchange of ideas, and support the development of next generation tools and methodologies for intelligent data analysis.
Topics of interest include, but are not limited to the following
Data Mining Foundations
Theoretical Foundations of Data Mining
Scalable, Parallel and Distributed Data Mining
Mining Data Streams and Real Time Analytics
Graph, Network and Heterogeneous Information Network Mining
Graph Neural Networks, Graph Transformers and Dynamic Graph Learning
Spatial, Temporal and Spatio Temporal Data Mining
Text, Video, Audio and Multimedia Mining
Web, Social Media and Hypergraph Mining
Feature Engineering, Data Cleaning and Data Transformation
Data Integration and Fusion
Explainable Data Mining and Model Interpretability
Privacy Preserving Data Mining, Differential Privacy and Federated Learning
Adversarial Machine Learning and Robustness
Interactive Mining, Visualization and Human in the Loop Analytics
Automated Machine Learning (AutoML) and Neural Architecture Search
Optimization Methods for Large Scale Data Mining
Edge, IoT and Resource Constrained Data Mining
Data Centric AI: Data Quality, Weak Supervision and Data Governance
Foundation Models and Large Scale Pretrained Models for Data Mining
Generative AI, Diffusion Models and Synthetic Data Generation
Data Mining Applications
Bioinformatics, Genomics and Computational Biology
Healthcare, Medical Imaging and Clinical Decision Support
Biometrics and Identity Analytics
Financial Modeling, Fraud Detection and Risk Analytics
Time Series Forecasting and Sequential Modeling
Image, Video and Multimodal Analytics
Cybersecurity, Intrusion Detection and Threat Intelligence
Social Network Analysis and Social Computing
Educational Data Mining and Learning Analytics
E commerce, Recommender Systems and Personalization
Smart Cities, Transportation and Urban Computing
Environmental, Climate and Sustainability Analytics
Scientific Machine Learning (Physics, Chemistry, Materials, Climate)
Industrial, Manufacturing and IoT Data Mining
Legal, Policy and Ethical Applications of Data Mining
Human Behavior Modeling, Affective Computing and Behavioral Analytics
Reinforcement Learning for Recommendation, Planning and Optimization
Digital Twins, Simulation Driven Analytics and Synthetic EnvironmentsGeneration
Knowledge Processing
Data and Knowledge Representation
Knowledge Graphs, Semantic Technologies and Knowledge Enhanced ML
Knowledge Discovery Frameworks and Pipelines
Pre and Post Processing in Knowledge Discovery
Causal Inference, Causal Discovery and Counterfactual Reasoning
Predictive Modeling, Evaluation and Model Validation
Probabilistic and Statistical Methods for Knowledge Extraction
Interactive Knowledge Exploration and Visualization
Mining Trends, Emerging Patterns and Risk Analysis
Knowledge Extraction from Noisy, Incomplete, or Low Quality Sources
Hybrid AI Systems Combining Symbolic and Machine Learning Approaches
Reasoning, Inference and Decision Support Systems
Human Centered AI and User Driven Knowledge Discovery
Trustworthy AI: Fairness, Accountability, Transparency and EthicsGeneration
Systems, Infrastructure and Deployment
ML Systems, Data Pipelines and MLOps
Distributed Training and Inference Systems
Hardware Aware Model Design and Acceleration
Scalable Storage, Indexing and Retrieval for Data Mining
Deployment, Monitoring and Lifecycle Management of Data Mining Models
Important Dates
- Submission Deadline : February 08, 2026
- Notification : March 07, 2026
- Final Manuscript Due : March 14, 2026
- Publication Date : Determined by the Editor-in-Chief
Call for Papers
H-Index
