The method includes reading a data set including a sparse number of data points and applying multiple tests wherein the results are evaluated by a decision module to determine whether to classify the data as random or nonrandom. In one preferred embodiment, if any one test determines the data is nonrandom, then the data is labeled nonrandom. The data is labeled and stored prior to beginning the method once again for the next set of data.