TUNNEL CLUSTERING METHOD

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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.

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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