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Diskretnyi Analiz i Issledovanie Operatsii, 2022, Volume 29, Issue 1, Pages 18–32
DOI: https://doi.org/10.33048/daio.2022.29.713 (Mi da1290) 		 
Computational complexity of two problems of cognitive data analysis

O. A. Kutnenkoab

a Sobolev Institute of Mathematics, 4 Acad. Koptyug Avenue, 630090 Novosibirsk, Russia
b Novosibirsk State University, 2 Pirogov Street, 630090 Novosibirsk, Russia
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DOI: https://doi.org/10.33048/daio.2022.29.713
Abstract: The NP-hardness in the strong sense is proved for two problems of cognitive data analysis. One of them is the problem of taxonomy (clustering), i. e. splitting an unclassified sample of objects into disjoint subsets. The other is the problem of sampling a subset of typical representatives of a classified sample which consists of objects of two images. The first problem can be considered as a special case of the second problem, provided that one of the images consists of one object. To obtain a quantitative quality estimate for the set of selected typical representatives of the sample, the function of rival similarity (FRiS function) is used, which assesses the similarity of an object with the closest typical object. Illustr. 1, bibliogr. 18.
Keywords: NP-hardness, taxonomy (clustering), typical object (prototypes) selection, function of rival similarity.
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation FWNF-2022-0015
The study is carried out within the framework of the state contract of the Sobolev Institute of Mathematics (Project FWNF–2022–0015).
Received: 26.04.2021
Revised: 02.12.2021
Accepted: 03.12.2021
Bibliographic databases:
Document Type: Article
UDC: 519.87+519.854
Language: Russian
Citation: O. A. Kutnenko, “Computational complexity of two problems of cognitive data analysis”, Diskretn. Anal. Issled. Oper., 29:1 (2022), 18–32
Citation in format AMSBIB
\Bibitem{Kut22}
\by O.~A.~Kutnenko
\paper Computational complexity of two problems of~cognitive~data~analysis
\jour Diskretn. Anal. Issled. Oper.
\yr 2022
\vol 29
\issue 1
\pages 18--32
\mathnet{http://mi.mathnet.ru/da1290}
\crossref{https://doi.org/10.33048/daio.2022.29.713}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=4412506}
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    		Дискретный анализ и исследование операций
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    References:48
    First page:7
     
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