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Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing
P. 227–231.
This article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances are mainly published in artificial intelligence and FCA dedicated venues, a dissemination towards data mining and machine learning is worthwhile.
Publication based on the results of:
In book
* III. Vol. 9286. , Dordrecht, L., Heidelberg, NY, Cham: Springer, 2015.
Junyu B., Fei H., Huilin F. et al., International Journal of Approximate Reasoning 2025 Vol. 187 Article 109541
In Formal Concept Analysis (FCA), concept reduction serves as an important means of simplification. The application scenarios of concept reduction cover various aspects such as data mining, knowledge discovery, strategic decision-making, and rule learning. For symmetric formal contexts, a specialized class of concept reduction exists that can fully recover all knowledge. However, most existing concept ...
Added: December 1, 2025
Dudyrev E., Mariia Zueva, Kuznetsov S. et al., , in: FCA4AI 2024: The 12th International Workshop "What can FCA do for Artificial Intelligence?", October 19 2024, Santiago de Compostela, SpainVol. 3911.: CEUR Workshop Proceedings, 2024. P. 47–58.
Clustering aims at finding disjoint groups of similar objects in data and is one major task in Machine Learning. It is also gaining more attention in Formal Concept Analysis community in these last years. This paper proposes an original approach to the clustering of complex data based on Formal Concept Analysis (FCA) and Pattern Structures. ...
Added: April 30, 2025
CEUR Workshop Proceedings, 2024.
The eleven preceding editions of the FCA4AI Workshop showed that many researchers working in Articial Intelligence are deeply interested in a well-founded method for classication and data mining such as Formal Concept Analysis (see https://upriss.github.io/fca/fca.html).
The FCA4AI Workshop Series started with ECAI 2012 (Montpellier) and the last edition was co-located with IJCAI 2023 (Macao, China). The ...
Added: April 29, 2025
Sergei O. Kuznetsov, Parakal E. G., Lecture Notes in Networks and Systems 2023 Vol. 776 P. 423–434
Inherently explainable Machine Learning (ML) models are able to provide explanations for their predictions by virtue of their construction. The explanations of a ML model are more comprehensible if they are expressed in terms of its input features. Our paper proposes an inherently explainable pipeline for document classification using pattern structures and Abstract Meaning Representation ...
Added: February 5, 2024
Dudyrev E., Kuznetsov S., Napoli A., , in: FCA4AI 2023 What can FCA do for Artificial Intelligence 2023 Proceedings of the 11th International Workshop "What can FCA do for Artificial Intelligence?" co-located with the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Macao, S.A.R. China; August 20, 2023Vol. 3489.: CEUR-WS.org, 2023. P. 69–80.
Rule Learning and Formal Concept Analysis (FCA) are two fields of science that study similar topic yet speak in a very different terms. This paper describes rule-based machine learning models with FCA-based terminology which results in decision quiver model. A decision quiver, discussed in the paper, is a supervised machine learning model that is based ...
Added: October 4, 2023
Dudyrev E., Kuznetsov S., Napoli A., , in: 17th International Conference, ICFCA 2023, Kassel, Germany, July 17–21, 2023, Proceedings. Formal Concept Analysis, (LNCS, volume 13934).: Switzerland: Springer, 2023. P. 127–142.
In this paper we introduce and study description quivers as compact representations of concept lattices and respective ensembles of decision trees. Formally, description quivers are directed multigraphs where vertices represent concept intents and (multiple) edges represent generators of intents. We study some properties of description quivers and shed light on their use for describing state-of-the-art symbolic machine ...
Added: October 4, 2023
Ignatov D. I., Lobachevskii Journal of Mathematics 2023 No. 44 P. 137–146
We consider two ways how to compute the number of maximal antichains in the Boolean lattice on 𝑛 elements. The first one is based on full direct enumeration, while the second ones relies on concept lattices or Galois lattices (studied in Formal Concept Analysis, an applied branch of lattice theory) and the Dedekind–MacNeille completion of a partial ...
Added: June 13, 2023
Lukianchenko P., Gromov V., Beschastnov Y. et al., Вестник кибернетики 2022 Т. 4 № 48 С. 37–48
The study analyzes the time series of the number of new cases in the administrative courts
of the Russian Federation using two methods of time series grouping according to the chaotic, stochastic, and
regular structure. The first model is based on the entropy‒complexity plane, the second one is presented by the
attribute‒object graph. As a result, four groups ...
Added: March 20, 2023
Gromov V., Урманцева Н. Р., [б.и.], 2021.
В докладе рассматриваются подходы к прогнозированию на основе кластеризации, опирающиеся на методологию анализа формальных понятий. Методология применяется для кластеризации участков временного ряда с целью выделения характерных участков (мотивов), отвечающих больным с различной степенью засорённости фистулы. ...
Added: January 30, 2023
Ilya Semenkov, Sergei O. Kuznetsov, , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. P. 105–112.
This paper presents different versions of classification ensemble methods based on pattern structures. Each of these methods is described and tested on multiple datasets (including datasets with exclusively numerical and exclusively nominal features). As a baseline model Random Forest generation is used. For some classification tasks the classification algorithms based on pattern structures showed better ...
Added: December 19, 2022
Egurnov D., Точилкин Д. С., Ignatov D. I., , in: Complex Data Analytics with Formal Concept Analysis.: Springer, 2022. P. 239–258.
In this paper, we describe versions of triclustering algorithms adapted for efficient calculations in distributed environments with MapReduce model or parallelisation mechanism provided by modern programming languages. OAC-family of triclustering algorithms shows good parallelisation capabilities due to the independent processing of triples of a triadic formal context. We provide time and space complexity of the ...
Added: November 1, 2022
Springer, 2022.
FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept ...
Added: November 1, 2022
Galitsky B., Ilvovsky D., Goncharova E., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. P. 75–87.
Supported decision trees that have been first proposed to boost the performance and the explainability of the expert systems built upon the texts can become a great basis for the machine reading comprehension (MRC) systems. The supported decision tree is based on building and combining the corresponding discourse trees for the text passage. In this work, ...
Added: November 1, 2022
Dudyrev E., Kuznetsov S., , in: Proceedings of the 10th International Workshop "What can FCA do for Artificial Intelligence?"Vol. 3233.: CEUR Workshop Proceedings, 2022. P. 23–34.
Studies on Explainable Artificial Intelligence show that a model should be small in order to be human understandable. The restriction on the size of a model drastically reduces the space of possible solutions. Many rule learning models still rely on greedy algorithms for generating ensembles of decision trees. This paper discusses FCA-inspired mathematical and engineering ...
Added: November 1, 2022
Dudyrev E., Kuznetsov S., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. Ch. 9 P. 99–104.
Ensembles of decision trees, like Random Forests are efficient machine learning models with state-of-the-art prediction quality. However, their predictions are much less transparent than those of a single decision tree. In this paper, we describe a prediction model based on a single decision tree in terms of Formal Concept Analysis. We define a differential way ...
Added: December 8, 2021
Buzmakov A. V., Kuznetsov S., Makhalova T. et al., , in: Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021)Vol. 2972.: CEUR-WS, 2021. Ch. 2 P. 19–26.
Added: December 7, 2021
Kuznetsov S., Goncharova E., , in: Proceedings of the Fifth International Scientific Conference "Intelligent Information Technologies for Industry" (IITI'21)Vol. 330.: Springer, 2022. P. 410–420.
Added: October 28, 2021