ART neural network based clustering method produces best quality clusters of fingerprints in comparison to Self Organizing Map and K-Means Clustering Algorithms

In this paper, we present and compare three clustering approaches which group fingerprints according to its minutiae points locations. The best technique for grouping fingerprints is based on the ART1 clustering algorithm. We compare the quality of clustering of ART1 based clustering with k-mean clustering technique and self organizing neural network (SOM) [1] clustering algorithm in terms of intra-cluster distances. Our results show that the average intra-cluster distance of the clusters formed by SOM and k-means algorithm varies from 83.36 to 127.372 and 33.925 to 58.17 respectively while the average intra-cluster distance of clusters formed by ART1 based clustering technique varies from 4.55 to 13.06, which indicates the clusters formed by ART1 clustering approach are much compact and isolated as compare to self organizing map and k-means based clustering approaches.