Enviar artigo por via de e-mail TUNNEL CLUSTERING METHOD
- Autores: Aleskerov F.T.1,2, Myachin A.L.1,2, Yakuba V.I.1,2
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Afiliações:
- National Research University Higher School of Economics
- V. A. Trapeznikov Institute of Control Science of Russian Academy of Science
- Edição: Volume 520, Nº 1 (2024)
- Páginas: 29-34
- Seção: MATHEMATICS
- URL: https://ogarev-online.ru/2686-9543/article/view/280128
- DOI: https://doi.org/10.31857/S2686954324060052
- EDN: https://elibrary.ru/KLEIQU
- ID: 280128
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Resumo
We propose a novel method for rapid pattern analysis in high-dimensional numerical data, termed “tunnel clustering”. The main advantages of this method are its relatively low computational complexity, endogenous determination of cluster composition and number, and a high degree of interpretability of the final results. We present descriptions of three different variations: one with fixed hyperparameters, an adaptive version, and a combined approach. Three fundamental properties of tunnel clustering are examined. Practical applications are demonstrated on both synthetic datasets containing 100,000 objects and on classical benchmark datasets.
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Sobre autores
F. Aleskerov
National Research University Higher School of Economics; V. A. Trapeznikov Institute of Control Science of Russian Academy of Science
Email: alesk@hse.ru
Moscow, Russia; Moscow, Russia
A. Myachin
National Research University Higher School of Economics; V. A. Trapeznikov Institute of Control Science of Russian Academy of Science
Email: amyachin@hse.ru
Moscow, Russia; Moscow, Russia
V. Yakuba
National Research University Higher School of Economics; V. A. Trapeznikov Institute of Control Science of Russian Academy of Science
Email: yakuba@ipu.ru
Moscow, Russia; Moscow, Russia
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