Year: 2024
Type: article
Abstract: The main challenge in solving clustering problems using mathematical optimization techniques is the non-smoothness of the distance measure used. To overcome this challenge, we used Nesterov's smoothing technique to find a smooth approximation of the ℓ1 norm. In this study, we consider a bi-level hierarchical clustering problem where the similarity distance measure is induced from the ℓ1 norm. As a result, we are able to design algorithms that provide optimal cluster centers and headquarter (HQ) locations that minimize the total cost, as evidenced by the obtained numerical results.
Authors: Adugna Fita Gabissa, Legesse Lemecha Obsu
Institution: Adama Science and Technology University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,285
OpenAlex ID: W4402497127