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 that 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. The function of rival similarity (FRiS-function) is used, which assesses the similarity of an object with the closest typical object, to obtain a quantitative quality estimate for the set of selected typical representatives of the sample.
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The study was carried out within the framework of the state contract of Sobolev Institute of Mathematics, project no. 0314–2019–0015.
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Translated by V. Potapchouck
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Kutnenko, O.A. Computational Complexity of Two Problems of Cognitive Data Analysis. J. Appl. Ind. Math. 16, 89–97 (2022). https://doi.org/10.1134/S1990478922010082
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DOI: https://doi.org/10.1134/S1990478922010082