Comparing clusterings: an axiomatic view

This paper views clusterings as elements of a lattice. Distances between clusterings are analyzed in their relationship to the lattice. From this vantage point, we first give an axiomatic characterization of some criteria for comparing clusterings, including the variation of information and the unadjusted Rand index. Then we study other distances between partitions w.r.t these axioms and prove an impossibility result: there is no "sensible" criterion for comparing clusterings that is simultaneously (1) aligned with the lattice of partitions, (2) convexely additive, and (3) bounded.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Marina Meila
• Keywords: ICML 2005, Machine Learning, Unadjusted Rand Index