Parameterized Approximation Schemes for Clustering with General Norm Objectives

Author:

Abbasi Fateme1,Banerjee Sandip1,Byrka Jarosław2,Chalermsook Parinya3,Gadekar Ameet3,Khodamoradi Kamyar2,Marx Dániel4,Sharma Roohani5,Spoerhase Joachim6

Affiliation:

1. University of Wrocław,Poland

2. University of Regina,Canada

3. Aalto University,Finland

4. CISPA Helmholtz Center for Information Security,Germany

5. Max Planck Institute for Informatics,Germany

6. University of Sheffield,United Kingdom

Funder

European Research Council

Academy of Finland

Publisher

IEEE

Reference59 articles.

1. Linear-time approximation schemes for clustering problems in any dimensions

2. A PTAS for k-means clustering based on weak coresets

3. The hardness of k-means clustering;Dasgupta

4. The hardness of approximation of euclidean k-means;Awasthi

5. On coresets for k-means and k-median clustering

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1. Parameterized Inapproximability Hypothesis under Exponential Time Hypothesis;Proceedings of the 56th Annual ACM Symposium on Theory of Computing;2024-06-10

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