1. A method for compressing n-dimensional data, comprising: applying, by one or more processing modules, a data clustering algorithm to n-dimensional data to partition the n-dimensional data into one or more clusters, each of the one or more clusters comprising: a cluster center; and a cluster membership that comprises an index of one or more cluster members of the cluster; performing, by the one or more processing modules, for each of the one or more clusters, a subspace projection technique to generate, for each of the cluster members of the cluster, one or more projection coefficients for the cluster member; and performing, on the projection coefficients generated by the subspace projection technique, a tree-structured vector quantization; wherein resulting compressed n-dimensional data comprises, for each of the one or more clusters, a quantized cluster center for the cluster, one or more basis vectors for the cluster, and projection coefficients for the cluster, and wherein the n-dimensional data comprises an n-dimensional graph that comprises a plurality of points, each dimension corresponding to an attribute of an event in a network, the method further comprising determining an optimum number of clusters in which to partition the n-dimensional data by processing the n-dimensional data using a genetic algorithm, the genetic algorithm providing an indicator of an optimal number of clusters, the indicator of the optimal number of clusters being an input to the data clustering algorithm.