Related Experiment Video
Updated: Jun 28, 2025
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
15.6K
Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making
Surender Singh1, Abdul Haseeb Ganie1
1Faculty of Sciences, School of Mathematics, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir 182320 India.
Granular Computing
|April 16, 2024
Summary
This study introduces a novel correlation coefficient for picture fuzzy sets (PFSs), offering a more comprehensive measure of association. The new coefficient demonstrates superior performance in pattern recognition and decision-making tasks, including COVID-19 mask selection.
Area of Science:
- Fuzzy Set Theory
- Information Fusion
- Decision Science
Background:
- Picture fuzzy sets (PFSs) are effective for handling uncertainty and vagueness in complex assessments.
- Correlation coefficients are crucial for quantifying the association between PFSs across various scientific and engineering fields.
- Existing measures may not fully capture the nuanced relationships within picture fuzzy environments.
Purpose of the Study:
- To introduce a novel correlation coefficient for picture fuzzy sets.
- To demonstrate the advantages of the new coefficient over existing measures.
- To validate its effectiveness in pattern recognition and decision-making applications.
Main Methods:
- Development of a new correlation coefficient for picture fuzzy sets.
- Comparative analysis using correlation degree and linguistic hedges.
- Performance evaluation in pattern recognition tasks.
- Application to a real-world decision-making problem (COVID-19 mask selection).
Main Results:
- The proposed correlation coefficient provides a more holistic assessment of association between PFSs.
- It effectively captures both the extent and nature (positive/negative) of the relationship.
- Comparative analyses confirm its superiority over existing picture fuzzy correlation measures.
Conclusions:
- The new picture fuzzy correlation coefficient offers enhanced capabilities for analyzing uncertainty.
- It proves effective in pattern recognition and practical decision-making scenarios.
- This advancement contributes to more robust data analysis in fields utilizing fuzzy set theory.
Related Concept Videos
01:09Decision Making: P-value Method
5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K
01:53Cause and Effect
10.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.9K
01:09Correlation
11.7K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
11.7K
00:53Correlation and Regression
1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
02:20Correlations
32.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K
01:11Calculating and Interpreting the Linear Correlation Coefficient
5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K