1. A computer implemented method for hierarchical clustering of samples in a dataset, the method comprising: compressing the dataset to reduce a number of variables within each of the samples of the dataset; generating a nearest neighbor matrix identifying only nearest neighbor pairs between the samples based on differences between the variables of the samples and a corresponding proximity value associated with each of the nearest neighbor pairs; iteratively arranging the samples into a hierarchy that groups the samples based on the nearest neighbor matrix; and rendering the hierarchy to a display to graphically illustrate similarities or differences between the samples, wherein the nearest neighbor matrix is a separate data structure from the hierarchy that graphically illustrates similarities or differences between the samples, wherein the nearest neighbor matrix is iteratively updated to only identify updated nearest neighbor pairs with each iteration, and wherein each iteration of the separate data structure is used to generate a corresponding level of the hierarchy, wherein generating the nearest neighbor matrix comprises: calculating a vector component mean (VCM) for each of the samples; and for a given sample: calculating VCM differences between the VCM of the given sample and the VCM of all other samples; identifying a possible nearest neighbor to the given sample based at least indirectly on the VCM differences; calculating a distance using a proximity measure between the possible nearest neighbor and the given sample; and identifying a subset of additional possible nearest neighbors, base at least indirectly on the VCM differences and the distance calculated for just the possible nearest neighbor using the proximity measure, wherein a given nearest neighbor for the given sample is selected from amongst the possible nearest neighbor and the additional possible nearest neighbors, wherein the subset is less than all of the other samples.