Classification Recouvrante Bas\'ee sur les M\'ethodes \`a Noyau
Machine Learning
2012-11-30 v1 Computation
Methodology
Machine Learning
Abstract
Overlapping clustering problem is an important learning issue in which clusters are not mutually exclusive and each object may belongs simultaneously to several clusters. This paper presents a kernel based method that produces overlapping clusters on a high feature space using mercer kernel techniques to improve separability of input patterns. The proposed method, called OKM-K(Overlapping -means based kernel method), extends OKM (Overlapping -means) method to produce overlapping schemes. Experiments are performed on overlapping dataset and empirical results obtained with OKM-K outperform results obtained with OKM.
Cite
@article{arxiv.1211.6851,
title = {Classification Recouvrante Bas\'ee sur les M\'ethodes \`a Noyau},
author = {Chiheb-Eddine Ben N'Cir and Nadia Essoussi},
journal= {arXiv preprint arXiv:1211.6851},
year = {2012}
}
Comments
Les 43\`emes Journ\'ees de Statistique