Kernel clustering with automatic variable weighting for interval data

José Nataniel A. de Sá

doi:10.1016/j.neucom.2025.130849

Kernel clustering with automatic variable weighting for interval data

2025-07-07

0

PRE

AI

J

José Nataniel A. de Sá

M

Marcelo R.P. Ferreira

F

Francisco de A.T. de Carvalho

DOI：10.1016/j.neucom.2025.130849

Original

Original request for help

Save

Abstract

En 中文

• The paper provides new kernel-based clustering algorithms. • The paper considers kernel approaches in the original space and in the kernel space. • Four algorithms learn variable relevance weighs globally to cluster partition. • Two algorithms learn variable relevance weighs locally to each object cluster. • The product and sum constraints were imposed on the weights of the variables. • Experiments with interval datasets shows the usefulness of the algorithms.

Kernel clustering with automatic variable weighting for interval data

Abstract

Journal

Organization