The Gibbs--Rand Model

Thanks to its many applications the clustering ensemble problem has been extensively studied in the past. In htis problem we are giving in input $m$ clustering and the objective is to output a clustering that ``well-represent'' all the input clustering. In this paper, we propose to thee best of our knowledge the first generative model for the problem. Our model is parameterized by a ``center'' clustering and a scale; the probability of a particular clustering is an exponential function of its Rand distance to the center, scaled.
For this new model, we show: (i) a sampling algorithm that runs in polynomial time when the center has a constant number of clusters and (ii) a simple polynomial time reconstruction algorithm when the scale is small.

• Algorithms and Theory