EXTRACTION OF REPRESENTATIVE SUBSETS BY POTENTIAL FUNCTIONS METHOD AND GENETIC ALGORITHMS

Two procedures are suggested to select a representative subset from a large data set. The first is based on the use of the estimate of the m ultivariate probability density distribution by means of the potential functions technique. The first object selected for the subset is that for which the probability density is larger. Then, the distribution i s corrected, by subtraction of the contribution of the selected object multiplied by a selection factor. The second procedure uses genetic a lgorithms to individuate the subset that reproduces the variance-covar iance matrix with the minimum error. Both methods meet the requirement to obtain a representative subset, but the results obtained with the method based on potential functions are generally more satisfactory in the case when the original set is not a random sample from an infinit e population, but is the finite population itself. Several examples sh ow how the extraction of a representative subset from a large data set can give some advantages in the use of representation techniques (i.e ., eigenvector projection, non-linear maps, Kohonen maps) and in class modelling techniques. (C) 1998 Elsevier Science B.V. All rights reser ved.