Machine Learning for Data Mining
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 19 February 2026 | Viewed by 491
Special Issue Editors
Interests: big data analytics and stochastic optimization for renewable energy integration; data mining and data engineering; smart grids; embedded system and machine learning
Interests: big data analytics; data mining; data engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The field of machine learning (ML) has become the cornerstone of modern data mining, enabling the extraction of meaningful insights from complex, high-dimensional datasets. By leveraging advanced algorithms, neural networks, and statistical models, machine learning has enhanced data mining capabilities in various fields such as healthcare, cybersecurity, IoT, and intelligent systems. The rapid growth of data generated by digital platforms, edge computing, and distributed sensors has further expanded the need for scalable, efficient, and adaptive machine learning-driven data mining techniques. Recent breakthroughs in automated feature engineering, ensemble learning, and explainable AI have expanded the potential of data mining, enabling researchers and practitioners to develop robust predictive models, optimise decision-making processes, and discover hidden patterns in massive datasets. In addition, emerging challenges such as federated learning and real-time stream mining have also brought about exciting new research directions.
This Special Issue "Machine Learning for Data Mining" aims to showcase cutting-edge research and innovative methods at the intersection of machine learning and data mining. We encourage submissions exploring novel algorithms, scalable frameworks, and practical applications to address real-world challenges.
Original research articles and reviews are welcome. Potential topics include, but are not limited to, the following:
- Advanced machine learning algorithms;
- Deep learning in data mining;
- Scalable and distributed learning;
- Explainable AI in data mining;
- Privacy-preserving data mining;
- Real-time and streaming mining.
We look forward to your valuable contributions and groundbreaking research in this rapidly evolving field.
Dr. Yunchuan Liu
Dr. Lei Yang
Dr. Rui Wu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- data mining
- deep learning
- predictive modelling
- big data analytics
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