Symmetry and Asymmetry in Data Analysis
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 August 2025 | Viewed by 99
Special Issue Editors
Interests: data security; privacy preservation;cyber-physical system;data mining; big data
Interests: data analysis; cloud computing
Interests: big data; recommender systems; environmental protection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the realm of data analysis, symmetry and asymmetry are pivotal concepts that offer profound insights into the structure and distribution of data. Symmetry in data often implies a balance or uniformity, which can be indicative of a stable or predictable system. It is frequently observed in well-behaved datasets, where the distribution of data points is even across different categories or variables. This property can simplify analysis by allowing analysts to apply symmetrical models, such as the normal distribution, to make predictions or inferences with a high degree of confidence.
Conversely, asymmetry in data introduces complexity and variability, which can be challenging to model but also rich in information. Asymmetric data distributions, such as skewed or bimodal patterns, suggest underlying processes that may not conform to simple assumptions of symmetry. Analyzing asymmetry requires more nuanced approaches, such as using non-parametric tests or transforming data to better fit symmetrical models. The presence of asymmetry can reveal hidden patterns, outliers, or the influence of confounding variables that might be overlooked in a symmetrical analysis.
Prof. Dr. Song Deng
Prof. Dr. Xiong Fu
Prof. Dr. Di Wu
Guest Editors
Manuscript Submission Information
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Keywords
- data mining
- machine learning
- artificial intelligence
- data analysis for industrial information
- outlier identification
- data distribution
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