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 language | English |
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Title of host publication | Cyber Security, Cryptology, and Machine Learning - 8th International Symposium, CSCML 2024, Proceedings |
Editors | Shlomi Dolev, Michael Elhadad, Mirosław Kutyłowski, Giuseppe Persiano |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 79-91 |
Number of pages | 13 |
ISBN (Print) | 9783031769337 |
DOIs | |
State | Published - 2025 |
Event | 8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024 - Be'er Sheva, Israel Duration: 19 Dec 2024 → 20 Dec 2024 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 15349 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024 |
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Country/Territory | Israel |
City | Be'er Sheva |
Period | 19/12/24 → 20/12/24 |
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