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

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC digital library.