Provable Imbalanced Point Clustering

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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 languageAmerican English
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 - 31 Dec 2024
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

Conference

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

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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