Multi-dimensional Data Clustering and Visualization via Echo State Networks

Abstract

The chapter summarizes the proposed recently approach for multidimensional data clustering and visualization. It uses a special kind of recurrent networks called Echo state networks (ESN) to generate multiple two-dimensional (2D) projections of the multidimensional original data. For this purpose equilibrium states of all neurons in the ESN are exploited. In order to fit the neurons equilibriums to the data an algorithm for tuning internal weights of the ESN called Intrinsic Plasticity (IP) is applied. Next 2D projections are subjected to selection based on different criteria in dependence on the aim of particular clustering task to be solved. The selected projections are used to cluster and/or to visualize the original data set. Several examples demonstrate possible ways to apply the proposed approach to variety of multidimensional data sets, namely: steel alloys discrimination by their composition; Earth cover classification from hyper spectral satellite images; working regimes classification of an industrial plant using data from multiple measurements; discrimination of patterns of random dot motion on the screen; and clustering and visualization of static and dynamic “sound pictures” taken by multiple randomly placed microphones.