Finding centroid clusterings with entropy-based criteria

摘要

We investigate the following problem: Given a set of candidate clusterings for a common set of objects, find a centroid clustering that is most compatible to the input set. First, we propose a series of entropy-based distance functions for comparing various clusterings. Such functions enable us to directly select the local centroid from the candidate set. Second, we present two combining methods for the global centroid. The selected/combined centroid clustering is likely to be a good choice, i.e., top or middle ranked in terms of closeness to the true clustering. Finally, we evaluate their effectiveness on both artificial and real data sets.