Applications Based on Symmetry in Machine Learning and Data Mining
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 58
Special Issue Editors
Interests: AI; sustainable industries; machine learning; deep learning; user modeling; shared spaces; RAI
Special Issue Information
Dear Colleagues,
Symmetry, the principle of invariance under transformation, offers a robust framework for advancing machine learning and data mining. This Special Issue of Symmetry explores its applications, inviting contributions from a broad research community beyond traditional symmetry experts. In machine learning, symmetry enables models to generalize across variations, such as recognizing objects regardless of orientation, enhancing efficiency and robustness. In data mining, this facilitates the identification of stable patterns within complex datasets, applicable to domains such as time series analysis or network structures. This Special Issue seeks original research, reviews, and theoretical insights that leverage symmetry to address contemporary challenges. Potential topics include symmetry-informed neural network designs, feature extraction methods, clustering techniques, and dimensionality reduction approaches.
We also encourage interdisciplinary perspectives, welcoming submissions that integrate concepts from fields such as physics, biology, or social sciences with computational methods. We aim to foster collaborative dialogue that advances both practical applications and theoretical understanding in these fields. Researchers at all career stages are invited to contribute, whether through empirical studies or foundational analyses, to explore how symmetry can refine methodologies and yield impactful results across diverse data-driven disciplines.
Dr. Hao Chen
Dr. Qixia Zhang
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. Symmetry is an international peer-reviewed open access monthly 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
- symmetry
- machine learning
- data mining
- invariance
- model generalization
- pattern detection
- interdisciplinary research
- neural networks
- feature extraction
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