An O(N) clustering method on ultrametric data

We propose in this paper a novel clustering algorithm in ultrametric spaces. It has a computational cost of O(n). This method is based on the ultratriangle inequality property. Using the order induced by an ultrametric in a given space, we demonstrate how we explore quickly data proximities in this space. We present an example of our results and show the efficiency and the consistency of our algorithm compared with another.