1. A computer implemented method for clustering and visualization of an n-dimensional space in a Euclidean space, comprising: collecting a plurality of multi-dimensional data sets; estimating a probability density function from each data set; approximating a dissimilarity between every pair of said probability density functions and recording every dissimilarity in a dissimilarity matrix; embedding the dissimilarity matrix into a Euclidean space having a dimensionality of three or less using a multi-dimensional scaling method, where the steps of estimating, approximating and embedding are executed by a processor of a computing device; and graphically displaying relationships between data sets using data in the Euclidean space on a display of the computing device.