Method of particles in visual clustering of multi-dimensional and large data sets

A method dedicated for visual clustering of N-dimensional data sets is presented. It is based on the classical feature extraction technique - the Sammon's mapping. This technique empowered by a particle approach used in the Sammon's criterion minimization makes the method more reliable, general and efficient. To show its reliability, the results of tests are presented, which were made to exemplify the algorithm 'immunity' from data errors. The general character of the method is emphasized and its role in multicriterial analysis discussed. Due to inherent parallelism of the methods, which are based on the particle approach, the visual clustering technique can be implemented easily in parallel environment. It is shown that parallel realization of the mapping algorithm enables the visualization of data sets consisting of more than 10(4) multi-dimensional data points. The method was tested in the PVM, MPI and data parallel environments on an HP/Convex SPP/1600. In this paper, the authors compare the parallel algorithm performance for these three interfaces. The approach to visual clustering, presented in the paper, can be used in visualization and analysis of large multi-dimensional data sets. (C) 1999 Elsevier Science B.V. All rights reserved.