An Alternative Approach of Similarity Measures for Decision-Making in Fuzzy Environments
Omdutt Sharma, Surender Kumar, Naveen Kumar
Abstract
In our daily life, we face the problems which involve uncertainty, imprecision and vagueness. To address these
challenges, wide ranges of methods and measures have been developed by the researchers. Among them,
similarity measures hold particular importance in the fields such as pattern recognition, data mining and decision-
making where the vagueness are involved. However, many existing similarity approaches suffer from limitations
arising from their complex mathematical structures, making them less accessible to the researchers without strong
mathematical backgrounds. In this research paper, we proposed two alternative approaches for decision-making
and pattern recognition in a fuzzy environment. These approaches are designed to be more intuitive, practical and
user-friendly. The first approach is based on the use of curves, providing an easily interpretable and visually
intuitive solution while the second approach involves calculating the area between curves requiring only minor
mathematical computation. A comparative analysis demonstrates that both proposed approaches serve as viable
and effective alternatives to traditional similarity measures. To validate the effectiveness and real-world
applicability of the proposed approaches, a case study in the field of physiotherapy is presented. The data on
patient symptom and associated disease information were collected from the Department of Physiotherapy, BMU,
Asthal Bohar, Rohtak, India.
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