A note on clustering aggregation for binary clusterings

Jiehua Chen, Danny Hermelin, Manuel Sorge

Abstract: We consider the clustering aggregation problem in which we are given a set of clusterings and want to find an aggregated clustering which minimizes the sum of mismatches to the input clusterings. In the binary case (each clustering is a bipartition) this problem was known to be NP-hard under Turing reductions. We strengthen this result by providing a polynomial-time many-one reduction. Our result also implies that no 2o(n)⋅|I′|O(1)-time algorithm exists that solves any given clustering instance I′ with n elements, unless the Exponential Time Hypothesis fails. On the positive side, we show that the problem is fixed-parameter tractable with respect to the number of input clusterings.