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AIMS Mathematics

2023, Volume 8, Issue 9: 21069-21088. doi: 10.3934/math.20231073
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Research article

Applications of picture fuzzy filters: performance evaluation of an employee using clustering algorithm

  • 1.
    Department of Mathematics, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
  • 2.
    Department of Computer Science, King Khalid University, Abha-62529, Saudi Arabia
  • Received: 03 May 2023 Revised: 13 June 2023 Accepted: 24 June 2023 Published: 03 July 2023

  • MSC : 03E72, 54A40

  • This article defines the concepts of picture fuzzy filter, picture fuzzy grill, picture fuzzy section, picture fuzzy base, picture fuzzy subbase, picture fuzzy ultrafilter, as well as their fundamental features. Characteristics of the aforementioned concepts are addressed, and equivalence between the picture fuzzy filter and picture fuzzy grills is established. Real-world examples are offered to demonstrate the advantages of picture fuzzy filters in the classification of sets using a clustering technique. Illustration is provided to show the advantages of picture fuzzy sets and the results are compared with intuitionistic fuzzy sets. Clustering technique is applied to the picture fuzzy filter collection reduces the computational process which helps the decision makers to classify the sets with fewer iterations.

    Citation: K. Tamilselvan, V. Visalakshi, Prasanalakshmi Balaji. Applications of picture fuzzy filters: performance evaluation of an employee using clustering algorithm[J]. AIMS Mathematics, 2023, 8(9): 21069-21088. doi: 10.3934/math.20231073

    Related Papers:

  • This article defines the concepts of picture fuzzy filter, picture fuzzy grill, picture fuzzy section, picture fuzzy base, picture fuzzy subbase, picture fuzzy ultrafilter, as well as their fundamental features. Characteristics of the aforementioned concepts are addressed, and equivalence between the picture fuzzy filter and picture fuzzy grills is established. Real-world examples are offered to demonstrate the advantages of picture fuzzy filters in the classification of sets using a clustering technique. Illustration is provided to show the advantages of picture fuzzy sets and the results are compared with intuitionistic fuzzy sets. Clustering technique is applied to the picture fuzzy filter collection reduces the computational process which helps the decision makers to classify the sets with fewer iterations.


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