Evaluation of Interestingness Measures for Ranking Discovered Knowledge

When mining a large database the number of patterns dis covered can easily exceed the capabilities of a human user to identify in teresting results To address this problem various techniques have been suggested to reduce and or order the patterns prior to presenting them to the user In this paper our focus is on ranking summaries generated from a single dataset where attributes can be generalized in many dif ferent ways and to many levels of granularity according to taxonomic hierarchies We theoretically and empirically evaluate thirteen diversity measures used as heuristic measures of interestingness for ranking sum maries generated from databases The thirteen diversity measures have previously been utilized in various disciplines such as information the ory statistics ecology and economics We describe ve principles that any measure must satisfy to be considered useful for ranking summaries Theoretical results show that only four of the thirteen diversity measures satisfy all of the principles We then analyze the distribution of the index values generated by each of the thirteen diversity measures Empirical re sults obtained using synthetic data show that the distribution of index values generated tend to be highly skewed about the mean median and middle index values The objective of this work is to gain some insight into the behaviour that can be expected from each of the measures in practice