Study on Hesitant Fuzzy Information Measures and Their Clustering Application

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.