A Note on Clustering Aggregation.
Jiehua Chen, Danny Hermelin, Manuel Sorge · arXiv (Cornell University) · 2018
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 reduction. We strengthen this result by providing a polynomial-time many-one reduction. Our result also implies that no $2^{o(n)} \cdot |I|^{O(1)}$-time algorithm exists for any 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.