CNN based unsupervised pattern classification for linearly and non linearly separable data sets
Giovanni Costantini, Daniele Casali, Massimo Carota · Cineca Institutional Research Information System (Tor Vergata University) · 2005
A novel algorithm for unsupervised classification of data sets made up of integer valued patterns by means of Cellular Neural Network (CNN) is proposed. The algorithm is suited both for linearly separable and non linearly separable data sets. The adopted CNN is n-dimensional and is based on a space-variant template - neighborhood order 1 - to cluster n-dimensional datasets. The choice of a CNN architecture allows a straightforward hardware implementation, particularly suited for bi-dimensional patterns.