Provable Imbalanced Point Clustering

David Denisov, Dan Feldman, Shlomi Dolev, Michael Segal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We suggest efficient and provable methods to compute an approximation for imbalanced point clustering, that is, fitting k-centers to a set of points in Rd, for any d,k≥1. To this end, we utilize coresets, which, in the context of the paper, are essentially weighted sets of points in Rd that approximate the fitting loss for every model in a given set, up to a multiplicative factor of 1±ε. In Sect. 3 we provide experiments that show the empirical contribution of our suggested methods for real images (novel and reference), synthetic data, and real-world data. We also propose choice clustering, which by combining clustering algorithms yields better performance than each one separately.

Original languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 8th International Symposium, CSCML 2024, Proceedings
EditorsShlomi Dolev, Michael Elhadad, Mirosław Kutyłowski, Giuseppe Persiano
PublisherSpringer Science and Business Media Deutschland GmbH
Pages79-91
Number of pages13
ISBN (Print)9783031769337
DOIs
StatePublished - 2025
Event8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024 - Be'er Sheva, Israel
Duration: 19 Dec 202420 Dec 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15349 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024
Country/TerritoryIsrael
CityBe'er Sheva
Period19/12/2420/12/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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