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TUNNEL CLUSTERING METHOD
- Authors: Aleskerov F.T.1,2, Myachin A.L.1,2, Yakuba V.I.1,2
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Affiliations:
- National Research University Higher School of Economics
- V. A. Trapeznikov Institute of Control Science of Russian Academy of Science
- Issue: Vol 520, No 1 (2024)
- Pages: 29-34
- Section: MATHEMATICS
- URL: https://rjeid.com/2686-9543/article/view/682687
- DOI: https://doi.org/10.31857/S2686954324060052
- EDN: https://elibrary.ru/KLEIQU
- ID: 682687
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Abstract
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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About the authors
F. T. 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. L. 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. I. 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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