Clustering via codeword-agglomerating neural networks

Abstract

This paper presents a neural solution for clustering without pre-specifying the number of clusters. The presented clustering approach incorporates a harmonic neural network (HNN) and a codeword-agglomerating neural network (ANN). The HNN harmonizes criteria of mean squared error and information entropy to exploit the substructure in the input data and find the fittest codewords. Following the HNN, ANN undergoes an unsupervised process that agglomerates the codewords in accordance with the input nature, and the input partition is autonomously determined when the agglomeration operation of ANN converges to a certain number of centroids that represent the clusters.