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Granular Computing

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06.07.2023 | ORIGINAL PAPER

Novel fuzzy similarity measures and their applications in pattern recognition and clustering analysis

verfasst von: Surender Singh, Koushal Singh

Erschienen in: Granular Computing | Ausgabe 6/2023

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Abstract

Fuzzy similarity measures are utilized to match two or more records and are essential to deal with data classification and pattern-matching problems. We have noticed that the existing studies on similarity measures in the classical fuzzy framework have certain issues, for example, inappropriate identification of structured linguistic variables, inappropriate classification results, etc. In this paper, we propose three new fuzzy similarity measures based on continuous functions and realize their advantages in connection with their application to pattern recognition and cluster analysis. The validity of clusters is also identified using the concept of cluster validity index. The experimental results demonstrate that the proposed similarity measures show higher accuracy in the identification of structured linguistic variables and a higher degree of confidence in the classification of unknown patterns. Several application examples with artificial and real data are utilized to demonstrate the credibility and advantages of the proposed similarity measures.

Vorheriger Artikel A multi-criteria comprehensive evaluation framework of online learning platform based on Pythagorean probabilistic linguistic information

Nächster Artikel A probabilistic dual hesitant fuzzy multi-attribute decision-making method based on entropy and cross-entropy

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Titel: Novel fuzzy similarity measures and their applications in pattern recognition and clustering analysis
verfasst von: Surender Singh
Koushal Singh
Publikationsdatum: 06.07.2023
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 6/2023
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-023-00393-y

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