Abstract. In the literature of data mining and statistics, numerous interestingness measures have been proposed to disclose succinct object relationships of association patterns. However, it is still not clear when a measure is truly effective in large data sets. Recent studies have identified a critical property, null-(transaction) invariance, for measuring event associations in large data sets, but many existing measures do not have this property. We thus re-examine the null-invariant measures and find interestingly that they can be expressed as a generalized mathematical mean, and there exists a total ordering of them. This ordering provides insights into the underlying philosophy of the measures and helps us understand and select the proper measure for different applications.
Han et al. REF compare different null-invariant measures and provide insights into similarities and differences among them.
2007, Association mining in large databases: A re-examination of its measures
Mladenic and A. Skowron (eds), Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases
Computer Science