Sciences & technologie. B, Sciences de l’ingénieur
Volume 0, Numéro 35, Pages 23-33
2012-06-30
Authors : Hebboul Amel . Hachouf Fella .
This paper presents a novel unsupervised and incremental learning technique for data clustering that are polluted by noise using neural network approaches. The proposed approach is based on a self-organizing incremental neural network. The design of two-layer neural network enables this system to represent the topological structure of unsupervised on-line data, reports the reasonable number of clusters and gives typical prototype patterns of every cluster without prior conditions such as a suitable number of nodes. To confirm the efficiency of the proposed learning mechanism, we present a set of experiments with artificial and real world data sets.
Incremental learning, neural network, unsupervised classification.
Bendiab Esma
.
Kholladi M. K
.
pages 31-35.
Ouanes Miyada
.
pages 38-46.
Ouadfeul Sid Ali
.
Zaourar Naima
.
Boudella Amar
.
Hamoudi Mohamed
.
pages 103-118.
Gourine B
.
pages 139-160.
Benahmed Khaldia
.
Belarbi Mostefa
.
Hariche Abdelhamid
.
Benyamina Abou El Hassan
.
pages 56-64.