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: 1 November 2024 | Viewed by 853

Special Issue Editors

School of Architecture and Art, Central South University, Changsha 410083, China
Interests: spatio-temporal data mining, geospatial artificial intelligence, and topological data analysis

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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
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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

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Keywords

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

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Published Papers (1 paper)

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Research

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 556
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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