Correlation of intuitionistic fuzzy sets in probability spaces

Hong, Dug Hun; Hwang, Seok Yoon

doi:10.1016/0165-0114(94)00330-a

Cited by 146 publications

(65 citation statements)

References 5 publications

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“…This example shows that ρ w (G 6 ,G i ) is decreasing in i, intuitively. But the methods of [8,10,12] have not decreasing behavior. For our method weighted function f interact on correlation value between two fuzzy numbers.…”

Section: Correlation Between Fuzzy Numbersmentioning

confidence: 99%

Correlation Coefficient Between Fuzzy Numbers Based On Central Interval

Saneifard¹,

Rasoul²

2012

Journal of Fuzzy Set Valued Analysis

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When we deal with crisp data, it is very common to find the correlation between variables. Here, we propose a method to calculate the correlation coefficient for fuzzy data, but rather than defining the correlation on the intuitionistic fuzzy sets like most of the previous works, we adopt the method from central interval. This interval can be used as a crisp set approximation with respect to a fuzzy quantity. This indices can be applied for comparison of fuzzy numbers namely fuzzy correlations in fuzzy environments and expert's systems. Finally some of their applications are mentioned.

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Section: Correlation Between Fuzzy Numbersmentioning

confidence: 99%

Correlation Coefficient Between Fuzzy Numbers Based On Central Interval

Saneifard¹,

Rasoul²

2012

Journal of Fuzzy Set Valued Analysis

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“…To tackle this weakness, a fuzzy correlation measure [5,12] can be used to augment the fuzzy support-confidence framework for fuzzy association rules. This leads to the fuzzy correlation rules of the form A ⇒ Β [fuzzy support, confidence.correlation].…”

Section: From Fuzzy Association Analysis To Fuzzy Correlation Analysismentioning

confidence: 99%

A Strategic Study of Mining Fuzzy Association Rules Using Fuzzy Multiple Correlation Measues

John¹,

Samuel²,

Prakash

2012

Journal of Algorithms & Computational Technology

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Two different data variables may behave very similarly. Correlation is the problem of determining how much alike the two variables actually are and association rules are used just to show the relationships between data items. Mining fuzzy association rules is the job of finding the fuzzy item-sets which frequently occur together in large fuzzy data set, where the presence of one fuzzy item-set in a record does not necessarily imply the presence of the other one in the same record. In this paper a new method of discovering fuzzy association rules using fuzzy correlation rules is proposed, because the fuzzy support and confidence measures are insufficient at filtering out uninteresting fuzzy correlation rules. To tackle this weakness, a fuzzy correlation measure for fuzzy numbers, is used to augment the fuzzy support-confidence framework for fuzzy association rules. We have extended the Apriori algorithm to fuzzy multiple correlation analysis, which is the new approach presented in this paper comparing to most of the previous works. A practical study over the academic behaviour of a particular school is done and some valuable suggestions are given, based on the results obtained.

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“…As many real-world data may be fuzzy, the concept of correlation has been extended to fuzzy environments [21,[24][25][26] and intuitionistic fuzzy environments [27][28][29][30][31][32][33]. For instance, Gerstenkorn and Manko [27] introduced the correlation coefficients of intuitionistic fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

Correlation Coefficients of Extended Hesitant Fuzzy Sets and Their Applications to Decision Making

Lü

Liang

2017

Symmetry

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Extended hesitant fuzzy sets (EHFSs), which allow the membership degree of an element to a set represented by several possible value-groups, can be considered as a powerful tool to express uncertain information in the process of group decision making. Therefore, we derive some correlation coefficients between EHFSs, which contain two cases, the correlation coefficients taking into account the length of extended hesitant fuzzy elements (EHFEs) and the correlation coefficients without taking into account the length of EHFEs, as a new extension of existing correlation coefficients for hesitant fuzzy sets (HFSs) and apply them to decision making under extended hesitant fuzzy environments. A real-world example based on the energy policy problem is employed to illustrate the actual need for dealing with the difference of evaluation information provided by different experts without information loss in decision making processes.

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Correlation of intuitionistic fuzzy sets in probability spaces

Cited by 146 publications

References 5 publications

Correlation Coefficient Between Fuzzy Numbers Based On Central Interval

Correlation Coefficient Between Fuzzy Numbers Based On Central Interval

A Strategic Study of Mining Fuzzy Association Rules Using Fuzzy Multiple Correlation Measues

Correlation Coefficients of Extended Hesitant Fuzzy Sets and Their Applications to Decision Making

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