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Proc. 11th International Conference on Formal Concept Analysis (ICFCA 2013)
Vol. 7880.
Springer, 2013.
Editor-in-chief: P. Cellier, F. Distel, Ganter B.
Kuznetsov S., , in : Proc. 11th International Conference on Formal Concept Analysis (ICFCA 2013). Vol. 7880.: Springer, 2013. P. 254-266.
Pattern structures, an extension of FCA to data with complex descriptions, propose an alternative to conceptual scaling (binarization) by giving direct way to knowledge discovery in complex data such as logical formulas, graphs, strings, tuples of numerical intervals, etc. Whereas the approach to classification with pattern structures based on preceding generation of classifiers can lead ...
Added: June 2, 2013
Language:
English
Keywords: FCA (Formal Concept Analysis)
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
Buzmakov A. V., Napoli A., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016). : M. : [б.и.], 2016. P. 89-96.
FCA is a mathematical formalism having many applications
in data mining and knowledge discovery. Originally it deals with binary
data tables. However, there is a number of extensions that enrich stan
dard FCA. In this paper we consider two important extensions: fuzzy
FCA and pattern structures, and discuss the relation between them. In
particular we introduce a scaling procedure that ...
Added: October 14, 2016
Goncharova E., Ilvovsky D., Galitsky B., , in : Proceedings of the 9th International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2021). Vol. 2972.: CEUR-WS, 2021. P. 51-58.
Added: October 28, 2021
Kaytoue M., Codocedo V., Buzmakov A. V. et al., , in : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings. * III. Vol. 9286.: Dordrecht, L., Heidelberg, NY, Cham : Springer, 2015. 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 ...
Added: October 23, 2015
Kashnitsky Y., , in : Supplementary Proceedings of the 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST 2014). Vol. 1197: Supplementary Proceedings of AIST 2014.: Ekaterinburg : CEUR Workshop Proceedings, 2014. Ch. 12. P. 69-80.
Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and ...
Added: August 28, 2014
Buzmakov A. V., Kuznetsov S., Napoli A., , in : 2017 IEEE 17th International Conference on Data Mining (ICDM). : New Orleans : IEEE, 2017. Ch. 89. P. 757-762.
A scalable method for mining graph patterns stable under subsampling is proposed.
The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far.
We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns.
The ...
Added: September 26, 2017
Parinov A., Научно-техническая информация. Серия 2: Информационные процессы и системы 2014
В статье рассматриваются сочетания базовых структур данных локального хранилища системы поддержки принятия решений FCART и приводятся временные характеристики при использовании больших объемов данных. ...
Added: November 19, 2013
Buzmakov A. V., Kuznetsov S., Napoli A., , in : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings. * 2. Vol. 9285.: Dordrecht, L., Cham, Heidelberg, NY : Springer, 2015. P. 157-172.
In pattern mining, the main challenge is the exponential explosion of the set of patterns. Typically, to solve this problem, a constraint for pattern selection is introduced. One of the first constraints proposed in pattern mining is support (frequency) of a pattern in a dataset. Frequency is an anti-monotonic function, i.e., given an infrequent pattern, ...
Added: October 22, 2015
Borchmann D., Hanika T., Obiedkov S., , in : Formal Concept Analysis: 14th International Conference, ICFCA 2017, Rennes, France, June 13-16, 2017, Proceedings. Vol. 10308.: Cham : Springer, 2017. P. 72-88.
We revisit the notion of probably approximately correct implication bases from the literature and present a first formulation in the language of formal concept analysis, with the goal to investigate whether such bases represent a suitable substitute for exact implication bases in practical use cases. To this end, we quantitatively examine the behavior of probably approximately correct ...
Added: June 25, 2017
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
Buzmakov A. V., Egho E., Jay N. et al., International Journal of General Systems 2016 Vol. 45 No. 2 P. 135-159
Nowadays data-sets are available in very complex and heterogeneous ways. Mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of “complex” sequential data by means of interesting sequential patterns. We approach the problem using the elegant mathematical framework of ...
Added: February 25, 2016
Obiedkov S., Sertkaya B., Zolotukhin D., , in : 32nd International Workshop on Description Logics, DL 2019; Oslo; Norway; 18 June 2019 through 21 June 2019. : CEUR-WS.org, 2019. P. 1-10.
We propose an approach for approximately completing a TBox w.r.t. a fixed model. By asking implication questions to a domain expert, our method approximates the subsumption relationships that hold in expert’s model and enriches the TBox with the newly discovered relationships between a given set of concept names. Our approach is based on Angluin’s exact ...
Added: October 29, 2019
Ryzhova D., Obiedkov S., , in : CLLS 2016. Computational Linguistics and Language Science. Proceedings of the Workshop on Computational Linguistics and Language Science. Moscow, Russia, April 26, 2016. Vol. 1886.: Aachen : CEUR Workshop Proceedings, 2017. Ch. 10. P. 78-87.
In this paper, we present an application for formal concept analysis (FCA) by showing how it can help construct a semantic map for a lexical typological study. We show that FCA captures typological regularities, so that concept lattices automatically built from linguistic data appear to be even more informative than traditional semantic maps. While sometimes ...
Added: October 14, 2017
Kuznetsov S., Neznanov A., Poelmans J., , in : Proceedings, Workshop “What can FCA do for Artificial Intelligence?” of the ECAI 2012 conference. : M. : CEUR Workshop Proceedings, 2012. Ch. 12. P. 81-87.
Software system Cordiet-FCA is presented, which is designed for knowledge discovery in big dynamic data collections, including texts in natural language. Cordiet-FCA allows one to compose ontology-controlled queries and outputs concept lattice, implication bases, association rules, and other useful concept-based artifacts. Efficient algorithms for data preprocessing, text processing, and visualization of results are discussed. Examples ...
Added: January 30, 2013
Borchmann D., Hanika T., Obiedkov S., Discrete Applied Mathematics 2020 Vol. 273 P. 30-42
We propose an algorithm for learning the Horn envelope of an arbitrary domain using an expert, or an oracle, capable of answering certain types of queries about this domain. Attribute exploration from formal concept analysis is a procedure that solves this problem, but the number of queries it may ask is exponential in the size ...
Added: October 29, 2019
Malaga : CEUR Workshop Proceedings, 2015
Social network analysis (SNA) is a multidisciplinary research area that has attracted many researchers from different disciplines such as Physics, Mathematics, Sociology, Biology and Computer Science, and has been studied according to different approaches and techniques. A social network is a dynamic structure (generally represented as a graph) of a set of entities/actors (nodes) together ...
Added: October 19, 2015
Ignatov D. I., Kaminskaya A. Y., Konstantinov A. V. et al., , in : Conceptual Structures for STEM Research and Education, 20th International Conference on Conceptual Structures. Vol. 7735: Conceptual Structures for STEM Research and Education, 20th International Conference on Conceptual Structures.: Berlin, Heidelberg : Springer, 2013. P. 173-192.
This paper considers a data analysis system for collaborative platforms which was developed by the joint research team of the National Research University Higher School of Economics and the Witology company. Our focus is on describing the methodology and results of the first experiments. The developed system is based on several modern models and methods ...
Added: October 10, 2013
Prague : CEUR Workshop Proceedings, 2014
The first and the second edition of the FCA4AI Workshop showed that many researchers working in Artificial Intelligence are indeed interested by a well-founded method for classi- fication and mining such as Formal Concept Analysis (see http://www.fca4ai.hse.ru/). The first edition of FCA4AI was co-located with ECAI 2012 in Montpellier and published as http://ceur-ws.org/Vol-939/ while the ...
Added: September 12, 2014
Kashnitsky Y., Труды Московского физико-технического института 2014 Т. 6 № 3 С. 43-56
Triclustering is an outgrowth of Formal Concept Analysis intented to detect groups of objects with similar properties (clusters) in a context of three sets of entities. In case of social network analysis, for instance, these sets might be users, their interests and events they take part in. Triclustering here can help to detect users with ...
Added: November 8, 2013
Ignatov D. I., Egurnov D., Точилкин Д. С., , in : Supplementary Proceedings ICFCA 2019 Conference and Workshops. Vol. 2378.: CEUR Workshop Proceedings, 2019. P. 137-151.
This paper presents further development of distributed multimodal clustering. We introduce a new version of multimodal clustering algorithm for distributed processing in Apache Hadoop on computer clusters. Its implementation allows a user to conduct clustering on data with modality greater than two. We provide time and space complexity of the algorithm and justify its relevance. ...
Added: October 31, 2019
Nenova Elena, Ignatov D. I., Konstantinov A. V., , in : Formal Concept Analysis Meets Information Retrieval 2013. Vol. 977.: Aachen : CEUR Workshop Proceedings, 2013. P. 57-73.
We propose a new approach for Collaborative ltering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (Movielens dataset) we compare the approach with the SVD- and NMF-based algorithms in terms of Mean Average Error (MAE). One of the experimental con- sequences is that ...
Added: October 10, 2013
Springer, 2019
This book constitutes the proceedings of the 15th International Conference on Formal Concept Analysis, ICFCA 2019, held in Frankfurt am Main, Germany, in June 2019.
The 15 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 36 submissions. The book also contains four invited contributions in full paper length.
The ...
Added: October 29, 2019
Neznanov A., Parinov A., , in : Artificial Intelligence: Methodology, Systems, and Applications 16th International Conference, AIMSA 2014, Varna, Bulgaria, September 11-13, 2014. Proceedings. Vol. 8722.: Dordrecht, L., Cham, Heidelberg, NY : Springer, 2014. P. 214-221.
Formal Concept Analysis Research Toolbox (FCART) is an integrated environment for knowledge and data engineers with a set of research tools based on Formal Concept Analysis. FCART allows a user to load structured and unstructured data (including texts with various metadata) from heterogeneous data sources into local data storage, compose scaling queries for data snapshots, and then ...
Added: October 14, 2014
Aachen : CEUR Workshop Proceedings, 2013
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classication, introduced and detailed in the book of Bernhard Ganter and Rudolf Wille, \Formal Concept Analysis", Springer 1999. The area came into being in the early 1980s and has since then spawned over 10000 scientic publications and a variety of practically ...
Added: October 10, 2013