Heuristic for Ranking the Interestigness of Discovered Knowledge

We describe heuristics, based upon information theory and statistics, for ranking the interestingness of summaries generated from databases. The tuples in a summary are unique, and therefore, can be considered to be a population described by some probability distribution. The four interestingness measures presented here are based upon common measures of diversity of a population: variance, the Simpson index, and the Shannon index. Using each of the proposed measures, we assign a single real value to a summary that describes its interesting-ness. Our experimental results show that the ranks assigned by the four interestingness measures are highly correlated.