Techniques allow automatic identification of statistically significant attribute combinations in a dataset, and provide users with an understanding thereof including starting points for further analysis. Statistically significant combinations may be obtained from large data sets by limiting combinations to four or fewer attributes. The combinations obtained may be ranked to differentiate patterns, e.g. according to factors such as error ratio, decision tree depth, occurrences, and number of attributes. Still further insights may be achieved by ranking attributes according to the number of statistically significant combinations in which they appear. For useful visualization of statistically significant information within the patterns, only those having at least one measure/numeric may analyzed for further insight (e.g. by an outlier algorithm) and presented as output in a chart (e.g. pie, bar) form. The decision tree approach of various embodiments may facilitate ‘What if’ analysis of the data, as well as obtaining the reverse inference.