- PII
- 10.31857/S2686954324060052-1
- DOI
- 10.31857/S2686954324060052
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume 520 / Issue number 1
- Pages
- 29-34
- 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.
- Keywords
- кластер кластеризация кластерный анализ туннельная кластеризация степень перехода
- Date of publication
- 15.02.2024
- Year of publication
- 2024
- Number of purchasers
- 0
- Views
- 46
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