Data Mining, Big Data, and Machine Learning: Theory and Applications

Dear Colleagues,

The rapid development of data-driven technologies, including data mining, big data, and machine learning, has significantly transformed both scientific research and real-world applications. These approaches are widely used across diverse domains such as economics and finance, healthcare, engineering, social sciences, cybersecurity, and artificial intelligence, enabling more efficient data analysis, prediction, and decision-making.

As data continues to grow in volume, velocity, and complexity, researchers face critical challenges related to scalability, computational efficiency, interpretability, and real-time processing. Addressing these challenges requires the integration of theoretical advances and practical solutions, as well as interdisciplinary collaboration across different research fields.

This Topic aims to provide a broad and flexible platform for contributions addressing both the theoretical foundations and practical applications of data mining, big data, and machine learning. It is designed to be relevant across multiple journals and disciplines, encouraging submissions from diverse research communities.

In addition to core methodological developments, this Topic also welcomes contributions that explore real-world applications, including those related to sustainable development, where data-driven approaches can support environmental monitoring, smart systems, resource optimization, and energy-efficient computing.

We invite high-quality original research articles, reviews, and case studies that contribute to advancing this rapidly evolving field.

Potential topics include, but are not limited to, the following:

• Data mining methods and knowledge discovery;
• Big data processing, storage, and analytics;
• Machine learning models, deep learning, and artificial intelligence;
• Explainable AI and interpretable machine learning;
• Optimization methods in data science;
• Time series analysis and forecasting;
• Network analysis and graph-based models;
• Stochastic modeling and uncertainty quantification;
• High-performance and distributed computing for big data;
• Cloud computing and edge computing in data analytics;
• Data security, privacy, and ethical aspects of AI;
• Applications in finance, economics, healthcare, engineering, and social systems;
• Smart cities and intelligent systems;
• Internet of Things (IoT) and data-driven applications;
• Data-driven approaches for sustainable development and environmental challenges;
• Sustainable data science and green AI: energy-efficient machine learning models, carbon-aware computing, and resource-efficient data processing.

Prof. Dr. Ionica Oncioiu
Prof. Dr. Guoxian Yu
Topic Editors