1. A method for dimensionality reduction of large data volumes comprising the steps of: a. providing a dataset Γ of data points given as vectors; b. building a weighted graph G on Γ with a weight function w ε , wherein w ε corresponds to a local coordinate-wise similarity between the coordinates in Γ; c. constructing a random walk on graph G via a Markov transition matrix P, which is derived from w ε ; d. performing a spectral decomposition of P to obtain right and left eigenvectors of P; e. projecting the data points in Γ onto the right eigenvectors of P to obtain a set of projection values Γ B for each data point, whereby Γ B represents coordinates in a reduced space; and f. using the set of projection values to perform an action selected from the group consisting of clustering of an image, segmentation of an image, clustering of communication network activity, detection of an intrusion in communication network activity and detection of a buildup leading to a computer crash.