Subjectively interesting alternative clusterings

We deploy a recently proposed framework for mining subjectively interesting patterns from data to the problem of alternative clustering, where patterns are sets of clusters (clusterings) in the data. This framework outlines how subjective interestingness of patterns (here, clusterings) can be quantified using sound information theoretic concepts. We demonstrate how it motivates a new objective function quantifying the interestingness of a clustering, automatically accounting for a user’s prior beliefs and for redundancies between the discovered patterns.