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Symmetry
Special Issues
Applications Based on Symmetry/Asymmetry in Data Mining
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Applications Based on Symmetry/Asymmetry in 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 1966

Special Issue Editors

Dr. Jiawei Zhu

E-Mail Website
Guest Editor
School of Architecture and Art, Central South University, Changsha 410083, China
Interests: spatio-temporal data mining, geospatial artificial intelligence, and topological data analysis
Prof. Dr. Dongpo Xu

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
Interests: neural networks; deep learning; machine learning; computer vision; natural language processing; stochastic optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the burgeoning field of data mining has witnessed remarkable advancements. Symmetry and asymmetry are fundamental concepts influencing various aspects of data mining, including pattern recognition, anomaly detection, and classification. As data mining techniques continue to evolve, understanding the role of symmetry and asymmetry becomes increasingly important for advancing the field.

This Special Issue aims to delve into the role of symmetry and asymmetry within data mining applications across various domains. We welcome submissions that address recent advancements, methodologies, and practical applications leveraging symmetry and asymmetry in data mining processes. Potential topics of interest include but are not limited to novel data mining algorithms incorporating symmetry/asymmetry principles; innovative feature engineering techniques guided by symmetry/asymmetry considerations; cutting-edge data analysis methodologies leveraging symmetry/asymmetry insights; and case studies illustrating the practical implications of symmetry/asymmetry in data mining applications.

Dr. Jiawei Zhu
Prof. Dr. Dongpo Xu
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

  • data mining
  • symmetry
  • asymmetry
  • pattern recognition
  • anomaly detection
  • classification
  • machine learning
  • feature engineering
  • data analysis
  • clustering

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Published Papers (2 papers)

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Research

28 pages, 10581 KiB  
Article
A Textual Semantic Analysis Framework Integrating Geographic Metaphors and GIS-Based Spatial Analysis Methods
by Yu Liu, Zhen Ren, Kaifeng Wang, Qin Tian, Xi Kuai and Sheng Li
Symmetry 2025, 17(7), 1064; https://doi.org/10.3390/sym17071064 - 4 Jul 2025
Viewed by 367
Abstract
Geographic information systems (GISs) have shown considerable promise in enhancing textual semantic analysis. Current textual semantic analysis methods face significant limitations in accurately delineating semantic boundaries, identifying semantic clustering patterns, and representing knowledge evolution. To address these issues, this study proposes a framework [...] Read more.
Geographic information systems (GISs) have shown considerable promise in enhancing textual semantic analysis. Current textual semantic analysis methods face significant limitations in accurately delineating semantic boundaries, identifying semantic clustering patterns, and representing knowledge evolution. To address these issues, this study proposes a framework that innovatively introduces GIS methods into textual semantic analysis and aligns them with the conceptual foundation of geographical metaphor theory. Specifically, word embedding models are employed to endow semantic primitives with comprehensive, high-dimensional semantic representations. GIS methods and geographical metaphors are subsequently utilized to project both semantic primitives and their relationships into a low-dimensional geospatial analog, thereby constructing a semantic space model that facilitates accurate delineation of semantic boundaries. On the basis of this model, spatial correlation measurements are adopted to reveal underlying semantic patterns, while knowledge evolution is represented using ArcGIS 10.7-based visualization techniques. Experiments on social media data validate the effectiveness of the framework in semantic boundary delineation and clustering pattern identification. Moreover, the framework supports dynamic three-dimensional visualization of topic evolution. Importantly, by employing specialized visualization methods, the proposed framework enables the intuitive representation of semantic symmetry and asymmetry within semantic spaces. Full article
(This article belongs to the Special Issue Applications Based on Symmetry/Asymmetry in Data Mining)
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16 pages, 2405 KiB  
Article
High Resilient Asymmetry and Anomaly Detection Based on Data Causality
by Zhiyong Hao, Chenhao Yu, Junyi Zhu and Leilei Chang
Symmetry 2024, 16(7), 819; https://doi.org/10.3390/sym16070819 - 29 Jun 2024
Viewed by 939
Abstract
In the tunnel construction practice, multiple buildings’ tilt rate data are collected. In this study, data causality is defined to reflect the causal relation between the input and output of the building tilt rate detection data. Upon defining and calculating the data causality, [...] Read more.
In the tunnel construction practice, multiple buildings’ tilt rate data are collected. In this study, data causality is defined to reflect the causal relation between the input and output of the building tilt rate detection data. Upon defining and calculating the data causality, a new high resilient causality detection (HiReCau) method is proposed for abnormal building tilt rate detection. A numerical case and another practical case are studied for validation purposes. The case study results show that the proposed HiReCau method can accurately detect high-causality data and low-causality data among the building tilt rate detection data and produces superior results compared with the direct adoption of a machine learning approach. Furthermore, the resilience of HiReCau is validated by investigations testing varied levels of additional low-causality data in the training dataset. Presently, HiReCau is limited to handling problems with a single output. Furthermore, only the back-propagation neural network (BPNN) is tested as the baseline model and there is also room to further expand the data size. The proposed approach is versatile and able to be adjusted to handle fault diagnosis and safety assessment problems in varied theoretical and engineering backgrounds. Full article
(This article belongs to the Special Issue Applications Based on Symmetry/Asymmetry in Data Mining)
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