A Novel Mean-Shift Algorithm for Data Clustering - Institut d'Electronique et de Télécommunications de Rennes Accéder directement au contenu
Article Dans Une Revue IEEE Access Année : 2022

A Novel Mean-Shift Algorithm for Data Clustering

Résumé

We propose a novel Mean-Shift method for data clustering, called Robust Mean-Shift (RMS). A new update equation for point iterates is proposed, mixing the ones of the standard Mean-Shift (MS) and the Blurring Mean-Shift (BMS). Despite its simplicity, the proposed method has not been studied so far. RMS can be set up in both a kernel-based and a nearest-neighbor (NN)-based fashion. Since the update rule of RMS is closer to BMS, the convergence of point iterates is conjectured based on the Chen's BMS convergence theorem. Experimental results on synthetic and real datasets show that RMS in several cases outperforms MS and BMS in the clustering task. In addition, RMS exhibits larger attraction basins than MS and BMS for identical parametrization; consequently, its kernel variant requires a lower aperture of the kernel function, and its NN variant a lower number of nearest neighbors compared to MS or BMS, to achieve optimal clustering results. In addition, the NN version of RMS does not need to specify a convergence threshold to stop the iterations, contrarily to the NN-BMS algorithm.
Fichier principal
Vignette du fichier
A_Novel_Mean-Shift_Algorithm_for_Data_Clustering.pdf (1.79 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03593568 , version 1 (06-05-2022)

Licence

Paternité

Identifiants

Citer

Claude Cariou, Steven Le Moan, Kacem Chehdi. A Novel Mean-Shift Algorithm for Data Clustering. IEEE Access, 2022, 10, pp.14575-14585. ⟨10.1109/ACCESS.2022.3147951⟩. ⟨hal-03593568⟩
37 Consultations
269 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More