H.A. Kravtsov
Èlektron. model. 2018, 38(1):73-86
https://doi.org/10.15407/emodel.38.01.073
ABSTRACT
The article presents results of theoretical research of a model of calculations over classifications. Based on the mathematical structure tree a set of operations is proposed, which permits one to determine a measure for plane classifications. Classifications with several division planes are a metric space and are considered as logical development of plane classification used in the problems of experts’ selection. It has been shown that the obtained model satisfies principles of mathematical modeling.
KEYWORDS
flat classification, spatial classification, measure, relative distance, absolute distance, computing model.
REFERENCES
1. Association for Information Science and Technology (2015), Conceptual Crowbars and Classification at the Crossroads: The Impact and Future of Classification Research (SIG/CR), available at: https://www.asist.org/events/annual-meeting/annual-meeting-2015/seminars-and-workshops/conceptual-crowbars-and-classification-at-the-crossroads-the-impact-and-future-ofclassification-research-sigcr/ (accessed December, 2015).
2. Orlov, A.I. (2011), Organizatsionno-economicheskoe modelirovanie: teoriya prinyatiya resheniy [Organizational-economicmodeling:decisionmaking theory], KNORUS,Moscow, Russia.
3. Universal Decimal Classification, available at: http://www.udcsummary.info/ [accessed December, 2015]
4. Saaty, T. (1993), Prinyatie resheniy. Metod analiza ierarhiy [Decission making. Hierarchy analysis method], Radio i svyaz, Moscow, Russia.
5. Totsenko, V. (2002), Metody i sistemy podderzhki prinyatiya resheniy. Algoritmicheskiy aspect [Methods and systems of decision making support. Algorithmic aspect], Naukova dumka, Kiev, Ukraine.
6. Sheremetyeva, S.O. and Osminin, P.G. Kiberleninka. The methods and models of automatic keyword extracting, available at: http://cyberleninka.ru/article/n/metody-i-modeli-avtomaticheskogo-izvlecheniya-klyuchevyh-slov (accessed December, 2015)
7. Ivlev, Yu.V. (2008), Logika [Logic],Manual, 4th ed., rev. and ad., Prospekt,Moscow, Russia.
8. Feys,K. (1977), Algebra: koltsa, moduli i kategorii. Tom I [Algebra: rings,modules and categories. Vol. 1], Mir, Moscow, Russia.
9. Berztiss, A. (1974), Struktury dannykh [Data structures], Statistika, Moscow, Russia.
10. Shatalkin, A.I. (2012), Taksonomiya. Osnovy, printsipy i pravila [Taxonomy. Fundamentals, principles and rules], Tovarishchestvo nauchnykh publikatsiy «KMK», Moscow, Russia.
11. Zagoruyko, N.G., Borisova, I.A., Dyubanov, V.V. and Kuntenko, O.A. (2009), “Extents of similarity, compactness, informativeness and uniformity of a training sample”, Trudy vserossiyskoi kohferentsii “Znaniya — Ontologii — Teorii” (ZONT-09) [Proceedings of All-Russian Conf. Knowledge-Ontology-Theories (ZONT-09)], Novosibirsk, 2009, Vol. 1.,
pp. 93-102.
12. Eliseeva, I.I. and Rukavishnikov, V.O. (1977), Gruppirovka, korrelyatsiya, raspoznavaniye obrazov. Statisticheskiye metody klassifikatsii i izmereniya svyazei [Grouping, correlation and image recognition. Statistical methods of classification and relation measurement], Statistika, Moscow, Russia.
13. Bogdanov, A.A. (2003), Tektologiya: Vseobshchaya organizatsionnaya nauka [Tectology: Universal organizational science], Finansy, Moscow, Russia.
14. Samarsky, A.A. and Mikhailov, A.P. (2001), Matematicheskoye modelirovaniye: Idei, metody, primery [Mathematical modeling: Ideas, methods, examples], Fizmatlit, Moscow, Russia.
15. Beer S.T. (2005), Mozg firmy [Brain of the firm], Editorial URSS, Moscow, Russia.