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Pokrovsky boulevard, 11, room S938, Moscow, Russia, 109028
Phone: +7 (495) 772-95-90*27319
The School of Data Analysis and Artificial Intelligence was created in 2014 as part of the Department of Data Analysis and Artificial Intelligence. The school consists of world-renowned researchers who actively participate in international research projects.
Acquaye F. L., Kertesz-Farkas A., Stafford Noble W.
Journal of Proteome Research. 2023. Vol. 22. No. 2. P. 577-584.
Vasilii A. Gromov, Yury N. Beschastnov, Korney K. Tomashchuk.
PeerJ Computer Science. 2023. Vol. 9. No. .
Makhalova T., Kuznetsov S., Napoli A.
Data Mining and Knowledge Discovery. 2022. P. 108-145.
Dudyrev E., Semenkov Ilia, Kuznetsov S. et al.
Plos One. 2022. Vol. 17. No. 10.
Zhirayr Hayrapetyan, Nascimento S., Trevor F. et al.
In bk.: Information Systems and Technologies: WorldCIST 2022, Volume 2. Iss. 469. Springer, 2022. P. 141-147.
Dudyrev F., Neznanov A., Anisimova K.
In bk.: Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium -23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part II. Springer, 2022. P. 436-439.
Egurnov D., Точилкин Д. С., Ignatov D. I.
In bk.: Complex Data Analytics with Formal Concept Analysis. Springer, 2022. P. 239-258.
Egurnov D., Ignatov D. I.
Automation and Remote Control. 2022. Vol. 83. No. 6. P. 894-902.
Kudriavtseva P., Kashkinov M., Kertész-Farkas A.
Journal of Proteome Research. 2021. Vol. 20. No. 10. P. 4708-4717.
Kanovich M., Kuznetsov S., Scedrov A.
Information and Computation. 2022. Vol. 287.
School Head —Sergei Kuznetsov at the CORE 2018 Conference
School Head —Sergei Kuznetsov gave a keynote speech "Knowledge Discovery in Complex Data with Pattern Structures" at CORE 2018 congress held at the Center of Research in Computation of Politechnical Institute of Mexico (CENTRO DE INVESTIGACIÓN EN COMPUTACIÓN (CIC-IPN))
Mexico city, September 24-27
Abstract of the talk:
Formal Concept Analysis (FCA) provides a convenient tool for mining dependencies, clusters, and taxonomies from data, however data should be reduced (scaled) to binary form.
Pattern structures is an extension of Formal Concept Analysis that allows direct processing objects with arbitrary ordered descriptions like graphs (ordered by subgraph isomorphism), strings, etc.
We show different models of knowledge discovery like mining implications, association rules, biclusters, taxonomies, classification rules etc. can be performed on complex data using pattern structures.
Various applications of these approaches in chemoinformatics, NLP, medical informatics, bioinformatics are considered.