Vestnik KRAUNC. Fiz.-Mat. Nauki. 2022. vol. 40. no. 3. pp. 199–210. ISSN 2079-6641

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MSC 68T27

Research Article

Using multivalued logic for qualitative data analysis

L. A. Lyutikova

Institute of Applied Mathematics and Automation KBSC RAS, 360000, Nalchik, Shortanova st., 89a, Russia

The article considers a logical approach to data analysis for solving the classification problem. The studied data is a set of objects and their features. As a rule, this is disparate heterogeneous information, and it is not enough for a reasonable application of probabilistic models. Therefore, logical algorithms are considered, which, under certain conditions, may be more adequate. For an expressive formal representation of the relationship between objects and their attributes, multivalued logic is used, and the number of values depends on the particular attribute. Therefore, a system of operations on variables with different domains is proposed. As a result, a decision function is built, which is a classifier of objects present in the studied data. The properties and possibilities of this function are analyzed. It is shown that a logical function, which is a conjunction in the space of rules that connect given objects with their characteristic features, unambiguously characterizes the initial data, divides the subject area into classes, has modifiability properties, satisfies the requirements of completeness and consistency in the given area. The paper also proposes an algorithm for its implementation.

Key words: intellectual system, predicate logic, predicate, decision function, class, subject area.

DOI: 10.26117/2079-6641-2022-40-3-199-210

Original article submitted: 30.10.2022

Revision submitted: 10.11.2022

For citation. Lyutikova L. A. Using multivalued logic for qualitative data analysis. Vestnik KRAUNC. Fiz.-mat. nauki. 2022, 40: 3, 199-210. DOI: 10.26117/2079-6641-2022-40-3-199-210

Competing interests. The authors declare that there are no conflicts of interest regarding authorship and publication.

Contribution and Responsibility. All authors contributed to this article. Authors are solely responsible for providing the final version of the article in print. The final version of the manuscript was approved by all authors.

The content is published under the terms of the Creative Commons Attribution 4.0 International License (

© Lyutikova L. A., 2022


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Lyutikova Larisa Adolfovna – Ph. D. (Phys.& Math.) Head of the Dep., Neural Networks and Machine Learning, Institute of Applied Mathematics and Automation, Kabardino-Balkar Republic, Nalchik, Russia, ORCID 0000-0003-4941-7854.