Minimum spanning tree hierarchical clustering algorithm: A new Pythagorean fuzzy similarity measure for the analysis of functional brain networks
作者：
Highlights：
• The paper tackles the situations where network parameters may be uncertain.
• We let the network parameters to take the form of Pythagorean fuzzy (PF) numbers.
• We propose a graph theory-based agglomerative hierarchical clustering technique.
• A minimum spanning tree agglomerative hierarchical clustering method is used.
• A functional brain network is used to prove its practicality and efficiency.
摘要
•The paper tackles the situations where network parameters may be uncertain.•We let the network parameters to take the form of Pythagorean fuzzy (PF) numbers.•We propose a graph theory-based agglomerative hierarchical clustering technique.•A minimum spanning tree agglomerative hierarchical clustering method is used.•A functional brain network is used to prove its practicality and efficiency.
论文关键词：Hierarchical clustering,Minimum spanning tree,Generalized Pythagorean fuzzy number,Distance measure,Similarity measure
论文评审过程：Received 19 December 2021, Revised 4 February 2022, Accepted 27 March 2022, Available online 8 April 2022, Version of Record 19 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117016
