An incremental parallel neural network for unsupervised classification

Abstract

This paper presents a novel unsupervised and parallel 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 data sets and real world data sets.