Mining Interesting Patterns Using Estimated Frequencies from Subpatterns and Superpatterns

In knowledge discovery in databases, the number of discovered patterns is often too enormous for human to understand, so that filtering out less important ones is needed. For this purpose, a number of interestingness measures of patterns have been introduced, and conventional ones evaluate a pattern as how its actual frequency is higher than the predicted values from its subpatterns. These measures may assign high scores to not only a pattern consisting of a set of strongly correlated items but also its subpatterns, and in many cases it is unnecessary to select all these subpatterns as interesting. To reduce this redundancy, we propose a new approach to evaluation of interestingness of patterns. We use a measure of interestingness which evaluates how the actual frequency of a pattern is higher than the predicted not only from its subpatterns but also from its superpatterns. On the strength of adding an estimation from superpatterns, our measure can more powerfully filter out redundant subpatterns than conventional measures. We discuss the effectiveness of our interestingness measure through a set of experimental results.