A DC programming to two-level hierarchical clustering with ℓ1 norm

Résumé

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.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Voir l'article sur le site de la revue

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0