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<p>Neutrosophic fuzzy sets merge fuzzy logic with neutrosophic theory to better handle uncertain information by employing memberships of truth, indeterminacy, and falsity to evaluate fuzzy membership. This paper proposes new measures for similarity and distance, forming the basis for a unique clustering algorithm designed for neutrosophic fuzzy environments. Applied to educational data, this algorithm helps categorize students by their grade point averages and teacher evaluations, positively influencing high school graduation exam scores. Our method, validated through the DBI and SC, proves effective in identifying relevant subgroups for practical applications. This research not only demonstrates the algorithm’s ability to improve student performance but also offers insights for educational decision-making in student training.</p>

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Novel neutrosophic fuzzy max-min-based similarities and their application in clustering for educational decision-making support

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Quang-Thinh Bui,
  • Vaclav Snasel,
  • Witold Pedrycz,
  • Bay Vo

摘要

Neutrosophic fuzzy sets merge fuzzy logic with neutrosophic theory to better handle uncertain information by employing memberships of truth, indeterminacy, and falsity to evaluate fuzzy membership. This paper proposes new measures for similarity and distance, forming the basis for a unique clustering algorithm designed for neutrosophic fuzzy environments. Applied to educational data, this algorithm helps categorize students by their grade point averages and teacher evaluations, positively influencing high school graduation exam scores. Our method, validated through the DBI and SC, proves effective in identifying relevant subgroups for practical applications. This research not only demonstrates the algorithm’s ability to improve student performance but also offers insights for educational decision-making in student training.