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July 16, 2026
Team Success: Aligning Means with Objectives
In corporations, sports, and academia, people often face challenges they cannot handle alone. In such cases, selecting the right team is crucial. Tatiana Mayskaya, Associate Professor at the HSE Faculty of Economic Sciences and the International College of Economics and Finance, together with colleagues from foreign universities, examined team characteristics and found that less diverse teams are better suited to objectives where a high average performance is important, whereas more diverse teams are preferable when avoiding failure is critical. The paper has been published in Economic Theory.
July 15, 2026
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July 15, 2026
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Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings

Springer, 2021.
Academic editor: A. Braud, Buzmakov Aleksey, T. Hanika, F. Le Ber

This book constitutes the proceedings of the 16th International Conference on Formal Concept Analysis, ICFCA 2021, held in Strasbourg, France, in June/July 2021.

The 14 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 32 submissions. The book also contains four invited contributions in full paper length.

The research part of this volume is divided in five different sections. First, "Theory" contains compiled works that discuss advances on theoretical aspects of FCA. Second, the section "Rules" consists of contributions devoted to implications and association rules. The third section "Methods and Applications" is composed of results that are concerned with new algorithms and their applications. "Exploration and Visualization" introduces different approaches to data exploration.

Chapters
Decision Concept Lattice vs. Decision Trees and Random Forests
Dudyrev E., Kuznetsov S., , in: Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings.: Springer, 2021. Ch. 16 P. 252–260.
Added: September 28, 2021
Approximate Computation of Exact Association Rules
Bansal S., Kailasam S., Obiedkov S., , in: Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings.: Springer, 2021. P. 107–122.
We adapt a polynomial-time approximation algorithm for computing the canonical basis of implications to approximately compute frequent implications, also known as exact association rules. To this end, we define a suitable notion of approximation that takes into account the frequency of attribute subsets and show that our algorithm achieves a desired approximation with high probability. ...
Added: December 9, 2021
Research target: Mathematics Computer Science
Priority areas: IT and mathematics
Language: English
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DOI
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Keywords: машинное обучениеBoolean algebraknowledge managementанализ формальных понятийlogicанализ данныхdata miningsemantic webconcept latticesmachine learningformal concept analysisfuzzy setsassociation rules miningассоциативные правилаclusteringClosure systemgraph drawingartificial intelligenceUnsupervised learningPAC learningpartially ordered setsknowledge representation and reasoninglatticeknowledge exploration
Formal Concept Analysis: 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 – July 2, 2021, Proceedings
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