N. Tahiri

Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance

This paper introduces a method to infer multiple alternative consensus trees and supertrees using k-means clustering and Robinson and Foulds distance to capture diverse evolutionary patterns in phylogenetic trees.

A new clustering method for building multiple trees using deep learning.

This paper introduces a deep learning method to cluster phylogenetic trees, enabling the identification of homogeneous or heterogeneous groups of trees and improving efficiency for large datasets.