SUPR
Consistent 𝑘-center for clustering evolving data
Dnr:

NAISS 2025/22-123

Type:

NAISS Small Compute

Principal Investigator:

Aristides Gionis

Affiliation:

Kungliga Tekniska högskolan

Start Date:

2025-01-27

End Date:

2025-08-01

Primary Classification:

10201: Computer Sciences

Webpage:

Allocation

Abstract

This project is conducted by a WASP PhD student (Thibault Marette) and a WASP professor (Aris Gionis) in Theoretical Computer Science (TCS) at KTH. It aims at complementing a paper submission to the KDD 2025 conference. The paper presents a novel theoretical algorithm for consistent k-center clustering for evolving data, requiring experimental validation. We aim to run thorough experiments on large real-world datasets, to compare our algorithm with the state-of-the-art, on various metrics.