{"title":"Tunnel Clustering Method","authors":"F. T. Aleskerov, A. L. Myachin, V. I. Yakuba","doi":"10.1134/S1064562424702314","DOIUrl":null,"url":null,"abstract":"<p>We propose a novel method for rapid pattern analysis of high-dimensional numerical data, termed tunnel clustering. The main advantages of the method are its relatively low computational complexity, endogenous determination of cluster composition and number, and a high degree of interpretability of 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.</p>","PeriodicalId":531,"journal":{"name":"Doklady Mathematics","volume":"110 3","pages":"474 - 479"},"PeriodicalIF":0.5000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Doklady Mathematics","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1134/S1064562424702314","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a novel method for rapid pattern analysis of high-dimensional numerical data, termed tunnel clustering. The main advantages of the method are its relatively low computational complexity, endogenous determination of cluster composition and number, and a high degree of interpretability of 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.
期刊介绍:
Doklady Mathematics is a journal of the Presidium of the Russian Academy of Sciences. It contains English translations of papers published in Doklady Akademii Nauk (Proceedings of the Russian Academy of Sciences), which was founded in 1933 and is published 36 times a year. Doklady Mathematics includes the materials from the following areas: mathematics, mathematical physics, computer science, control theory, and computers. It publishes brief scientific reports on previously unpublished significant new research in mathematics and its applications. The main contributors to the journal are Members of the RAS, Corresponding Members of the RAS, and scientists from the former Soviet Union and other foreign countries. Among the contributors are the outstanding Russian mathematicians.