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Area of Science:

  • Information Science
  • Computer Science
  • Mathematics

Background:

  • Current hesitant fuzzy set research relies on equal length processing, which alters original data structures and information.
  • This data alteration poses a significant challenge for the advancement of hesitant fuzzy sets.

Purpose of the Study:

  • To address the limitations of equal length processing in hesitant fuzzy sets.
  • To propose novel measures for hesitant fuzzy information uncertainty, distance, and similarity.
  • To develop an effective hesitant fuzzy network clustering algorithm.

Main Methods:

  • Defined a hesitant fuzzy entropy function to quantify information uncertainty.
  • Introduced the concept of a hesitant fuzzy information feature vector.
  • Developed hesitant fuzzy distance and similarity measures based on the information feature vector.
  • Proposed a hesitant fuzzy network clustering method utilizing the similarity measure.

Main Results:

  • The proposed hesitant fuzzy entropy function effectively measures information uncertainty.
  • The hesitant fuzzy information feature vector provides a robust basis for distance and similarity calculations.
  • The hesitant fuzzy network clustering method demonstrates effectiveness in handling uncertain data.

Conclusions:

  • The developed methods overcome the drawbacks of equal length processing in hesitant fuzzy sets.
  • The novel approach enhances the analysis and clustering of hesitant fuzzy information.
  • The proposed algorithm offers a valuable tool for applications involving uncertain data.