1. A computerized method for discovering patterns in unknown multi-dimensional data, comprising: generating a time series of the multi-dimensional data; constructing a point cross-distance matrix by self-correlating the time series; locating all minimum cost paths in the point cross-distance matrix at a plurality of time resolutions; and relating the minimum cost paths to sub-sequences in the multi-dimensional data to discover high-level patterns in the unknown multi-dimensional data.