Automatic detection of patterns and inference in a dataset

摘要

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.

主权项

1. A computer-implemented method comprising:
providing a data set comprising data organized in rows and columns; generating a decision tree comprising a combination of between 2-4 columns; evaluating a statistical significance of the combination of columns; ranking the combination of columns; and based upon the ranked combination of columns, performing additional analysis selected from: ranking of columns; performing a “What if” analysis; and obtaining a reverse inference.