ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

K. I. Pakhomova, A. V. Korobko Application of Formal Concept Analysis for Intellectual Decision Support


The development of smart assistants in the information systems area has led to the increased interest of the scientific to artificial intelligence methods. This article focused on the study of modern directions of intellectual support of decision-making by using the method of algebraic lattice theory that is formal concept analysis (FCA). Most scientists apply this approach for a solution to a wide range of tasks in an artificial intelligence area. In this article, we have learned modern researchers used FCA for classification, recommendations, ontology, information retrieval, and feature selection. For each category of problems were analyzed the advantages of lattice theory in FCA case, also was described the solution of problems the possibilities of the FCA method. On the result, was defined the main advantages of the FCA method which were applied for the development of the author's theory of intellectual support of operational analytical processing of heterogeneous data on the basis of integral OLAP-model.


artificial intelligence, formal concept analysis, OLAP, intelligent decision support, analytical model.

PP. 37-46.

DOI 10.14357/20718594190405


1. Korobko A.V., Pen'kova T.G. Podderzhka operativnoj analiticheskoj obrabotki dannyh na osnove bazy znanij o kubah-konceptah, pp. 136–143., 2011.
2. Belohlavek R., Concept lattices and order in fuzzy logic. Annals of Pure and Applied Logic, Vol. 128, pp. 277– 298., 2004.
3. Ganter B., Wille R. Formal Concept Analysis. Springer-Verlag Berlin Heidelberg, 1999.
4. Davey B.A., Priestley H.A., Introduction to Lattices and Order. Cambridge: Cambridge University Press, 2002.
5. Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., & Dedene, G., Formal Concept Analysis in knowledge processing: A survey on models and techniques. Expert Systems with Applications, Vol. 40, pp. 6601-6623., 2013.
6. Kuznetsov, S.O., & Poelmans, J., Knowledge representation and processing with formal concept analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 3, pp. 200-215., 2013.
7. Kuznetsov S.O., Galois Connections in Data Analysis: Contributions from the Soviet Era and Modern Russian Research. In: B. Ganter, G. Stumme, R. Wille, Eds., Formal Concept Analysis: Foundations and Applications, Lecture Notes in Artificial Intelligence (Springer), Stateof- the Art Ser., Vol. 3626, pp. 196-225., 2005.
8. Obiedkov S., Modeling Ceteris Paribus Preferences in Formal Concept Analysis. In: Cellier P., Distel F., Ganter B. (eds) Formal Concept Analysis. ICFCA 2013. Lecture Notes in Computer Science, Springer, Berlin, Heidelber, Vol 7880, 2013.
9. Bogatyrev M. YU., Nuriahmetov V. R., Vakurin V. S. Metody analiza formal'nyh ponyatij v informacionnyh sistemah tekhnicheskoj podderzhki, Izvestiya TulGU, Tekhnicheskie nauki, 2013.
10. Poelmans, J. et al., Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research. In: Petra Perner, Ed., Proc. 11th Industrial Conference on Data Mining (ICDM 2012), Lecture Notes in Computer Science (Springer), Vol. 7377, pp. 273-287., 2012.
11. Carpineto C., Romano G., Using Concept Lattices for Text Retrieval and Mining. In: Ganter B., Stumme G., Wille R. (eds) Formal Concept Analysis. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, Vol. 3626, 2005.
12. Ignatov D.I., Introduction to Formal Concept Analysis and Its Applications in Information Retrieval and Related Fields. In: Braslavski P., Karpov N., Worring M., Volkovich Y., Ignatov D. (eds) Information Retrieval. RuSSIR 2014. Communications in Computer and Information Science, Springer, Cham, Vol. 505, 2015.
13. Finn V. K. O mashinno-orientirovannoj formalizacii pravdopodobnyh rassuzhdenij v stile F. Bekona – D. S. Millya. Semiotika i informatika, № 20, pp. 35–101., 1983.
14. Kuznecov S.O. Avtomaticheskoe obuchenie na osnove analiza formal'nyh ponyatij. Avtomat. i telemekh, № 10, pp. 3–27., 2001.
15. Prokasheva O., Onishchenko A., Gurov S. Classification Methods Based on Formal Concept Analysis. Formal Concept Analysis Meets Informational Retrivial. Workshop colocated with 35th European Conference on Informational Retrivial (ECIR 2013), pp. 1–10., 2013.
16. Onishchenko A.A., Gurov S.I., Klassifikaciya na osnove AFP i biklasterizacii: vozmozhnosti podhoda. Prikladnaya matematika i informatika Trudy fakul'teta Vychislitel'noj matematiki i kibernetiki, Vol. 38, pp. 77–87, 2011.
17. Prokasheva O., Efficiency improvement of the FCA-based classification algorythm. Vol. 1, pp. 550–556, 2013.
18. Trabelsi, M., Meddouri, N., & Maddouri, M., New Taxonomy of Classification Methods Based on Formal Concepts Analysis. FCA4AI@ECAI., Vol. 1703, pp. 113–120., 2016.
19. Sahami M., Learning classification rules using lattices (Extended abstract). In: Lavrac N., Wrobel S. (eds) Machine Learning: ECML-95. ECML 1995. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), Springer, Berlin, Heidelberg, Vol. 912, pp. 343–346., 1995.
20. Coulet A., Domenach F., Kaytoue M., Napoli A., Using Pattern Structures for Analyzing Ontology-Based Annotations of Biomedical Data. In: Cellier P., Distel F., Ganter B. (eds) Formal Concept Analysis. ICFCA 2013. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, Vol. 7880, 2013.
21. Bertet K. et al., Semantic Web: Big Data, Some Knowledge and a Bit of Reasoning. 2017.
22. Stumme, G., Maedche, A., FCA-MERGE: Bottom-Up Merging of Ontologies. IJCAI, pp. 225–234., 2001.
23. Cimiano P., Hotho A., Stumme G., Tane J., Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies. In: Eklund P. (eds) Concept Lattices. ICFCA 2004. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, Vol. 2961, 2004.
24. Benítez-Caballero M.J., Medina J., Ramírez-Poussa E., Attribute Reduction in Rough Set Theory and Formal Concept Analysis. In: Polkowski L. et al. (eds) Rough Sets. IJCRS 2017. Lecture Notes in Computer Science. Springer, Cham, Vol. 10313, pp. 513–525., 2017.
25. Liu, M., Shao, M., Zhang, W., & Wu, C., Reduction method for concept lattices based on rough set theory and its application. Computers & Mathematics with Applications, Vol. 53, pp. 1390–1410., 2007.
26. Kent R.E., Rough Concept Analysis. In: Ziarko W.P. (eds) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing. Springer, London, 1994.
27. Zhi H., Realization of Rough Set Approximation Toplogical Operations Based on Formal Concept Analysis. International Journal of Intelligence Science, Vol.4, pp. 65–69., 2014.
28. Cordero P. et al. Knowledge discovery in social networks by using a logic-based treatment of implications, Knowledge- Based Syst. Elsevier B.V.,Vol. 87, pp. 16–25., 2015.
29. Ignatov, D.I., Kuznetsov, S.O., Concept-based Recommendations for Internet Advertisement, Vol. 433, pp. 157– 166., 2008.
30. Ignatov D.I., Kuznecov S.O., Metody razrabotki dannyh (Data Mining) dlya rekomendatel'noj sistemy Internetreklamy. Odinnadcataya nacional'naya konferenciya po iskusstvennomu intellektu s mezhduna- rodnym uchastiem. Moskva, Lenand, 2008.
31. Medina J., Pakhomova K., Ramírez-Poussa E., Recommendation Solution for a Locate-Based Social Network via Formal Concept Analysis. In: Cornejo M., Kóczy L., Medina J., De Barros Ruano A. (eds) Trends in Mathematics and Computational Intelligence. Studies in Computational Intelligence. Springer, Cham, Vol. 796, pp. 131–138., 2019.
32. Lakhal L., Stumme G., Efficient Mining of Association Rules Based on Formal Concept Analysis. In: Ganter B., Stumme G., Wille R. (eds) Formal Concept Analysis. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, Vol. 3626, pp. 180–195., 2005.
33. Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., & Lakhal, L., Computing iceberg concept lattices with Titanic. Data Knowl. Eng, Vol. 42, pp. 189–222., 2002.
34. Abdullah Z., Saman M.Y.M., Karim B., Herawan T., Deris M.M., Hamdan A.R., FCA-ARMM: A Model for Mining Association Rules from Formal Concept Analysis. In: Herawan T., Ghazali R., Nawi N., Deris M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing. Springer, Cham, Vol. 549, pp. 213–223., 2017.