Clustering Permutations under the Ulam Metric: A Parameterized Complexity Study
Tian Bai, Fedor V. Fomin, Petr A. Golovach, Yash Hiren More, Simon Wietheger

TL;DR
This paper explores the parameterized complexity of rank aggregation problems under the Ulam metric, providing new algorithms and hardness results for various clustering variants.
Contribution
It introduces fixed-parameter algorithms for Ulam-based clustering problems and establishes complexity bounds, filling a gap in the understanding of these problems.
Findings
Ulam k-center is NP-hard for d=1 but FPT for k+d.
No polynomial kernel exists for k+d unless NP ⊆ coNP/poly.
Ulam k-median is W[1]-hard but admits an XP algorithm and a polynomial kernel.
Abstract
Rank aggregation seeks a representative permutation for a collection of rankings and plays a central role in areas such as social choice, information retrieval, and computational biology. Two fundamental aggregation tasks are the center and median problems, which minimize the maximum and the total distance to the input permutations, respectively. While these problems are well understood under Kendall's tau and related distances, their parameterized complexity under the Ulam metric, an edit-distance-based metric on permutations, has remained largely unexplored. In this work, we initiate a systematic study of the parameterized complexity of rank aggregation under the Ulam metric. We consider both the center and median problems, as well as their generalizations to the -center and -median clustering settings, parameterized by the number of centers and the distance budget …
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