Metric concentration search procedure using reduced matrix of pairwise distances

Subtitle:

Article type: Research Article

Authors: Katrutsa, A.M.^a | Kuznetsov, M.P.^a | Rudakov, K.V.^{a; b} | Strijov, V.V.^{a; b; *}

Affiliations: [a] Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia | [b] Dorodnicyn Computing Center of Russian Academy of Sciences, Vavilov st, Moscow, Russia

Correspondence: [*] Corresponding author: V.V. Strijov, Moscow Institute of Physics and Technology, Institutskiy lane 9, Dolgoprudny city, Moscow region, 141700, Russia. Tel.: +7 499 135 41 63; Fax: +7 499 783 33 27; E-mail:strijov@ccas.ru

Abstract: This paper presents a new fast clustering algorithm RhoNet, based on the metric concenration location procedure. To locate the metric concentration, the algorithm uses a reduced matrix of pairwise ranks distances. The key feature of the proposed algorithm is that it doesn't need the exhaustive matrix of pairwise distances. This feature reduces computational complexity. It is designed to solve the protein secondary structure recognition problem. The computational experiment collects tests and to hold performance analysis and analysis of dependency for the algorithm quality and structure parameters. The algorithm is compared with k-modes and tested on different metrics and data sets.

Keywords: Categorical data, ρ-net, metric concentration, pairwise distances matrix, fast clustering

DOI: 10.3233/IDA-150760

Journal: Intelligent Data Analysis, vol. 19, no. 5, pp. 1091-1108, 2015