Aims & Scope

Aims:

The Data Mining and Machine Learning (DMML) is an open access, per-reviewed journal that aims to publish novel research articles, reviews, and scientific reports in the field of Data Mining and Machine Learning. This journal provides a venue for global research on theoretical research and practical applications, fostering multidisciplinary dialogue and knowledge exchange among scientists, engineers, and industry professionals in related fields. It is published quarterly online by Scilight Press.

Scope:

DMML covers a comprehensive range of topics within the fields of data mining, machine learning, and artificial intelligence. Our priorities lie at the intersection of foundational theories and their practical applications, exploring how these methodologies can be leveraged to solve complex, real-world problems. The following sections highlight the core areas of focus:

Data Mining and Applications: Exploration of theoretical foundations of data mining, including data preparation, data underlying logic, data integration, data checking, data discovery, and frequent pattern.

Machine Learning and Applications: Exploration of theoretical foundations of machine learning, including traditional machine learning algorithms, semi-supervised learning, representation learning, domain-driven classification, deep learning, and quantum machine learning.

Big Data and Artificial Intelligence: Exploration of theoretical foundations and applications of big data and AI, including data quality, data recognition, advanced data mining, data-entred modeling, complex data association, knowledge representation, machine unlearning, multi-agent systems, and computational intelligence.

In addition to these foundational areas, DMML encourages research that bridges theory with practice, addressing emerging challenges and exploring interdisciplinary applications across various fields and industries.