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Next Priority Concept: A new and generic algorithm computing concepts from complex and heterogeneous data
P. 1-20.
In this article, we present a new data type agnostic algorithm calculating a concept lattice from heterogeneous and complex data. Our NextPriorityConcept algorithm is first introduced and proved in the binary case as an extension of Bordat's algorithm with the notion of strategies to select only some predecessors of each concept, avoiding the generation of unreasonably large lattices. The algorithm is then extended to any type of data in a generic way. It is inspired from pattern structure theory, where data are locally described by predicates independent of their types, allowing the management of heterogeneous data.
Publication based on the results of:
[б.и.], 2020
Theoretical Computer Science is mathematical and abstract in spirit, but it derives its motivation from practical and everyday computation. Its aim is to understand the nature of computation and, as a consequence of this understanding, provide more efficient methodologies. All papers introducing or studying mathematical, logic and formal concepts and methods are welcome, provided that their motivation is ...
Added: October 29, 2020
Buzmakov A. V., Egho E., Jay N. et al., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2013). Issue 1058.: Beijing : CEUR Workshop Proceedings, 2013. P. 7-14.
In this paper, we are interested in the analysis of sequential data and we propose an original framework based on Formal Concept Analysis (FCA). For that, we introduce sequential pattern structures, an original specification of pattern structures for dealing with sequential data. Pattern structures are used in FCA for dealing with complex data such as ...
Added: October 23, 2015
Birkhauser/Springer, 2017
This book constitutes the proceedings of the 23rd International Symposium on Foundations of Intelligent Systems, ISMIS 2017, held in Warsaw, Poland, in June 2017. The 56 regular and 15 short papers presented in this volume were carefully reviewed and selected from 118 submissions. The papers include both theoretical and practical aspects of machine learning, data mining ...
Added: September 18, 2017
Bernhard Ganter, Obiedkov S., Berlin, Heidelberg : Springer, 2016
This is the first textbook on attribute exploration, its theory, its algorithms for applications, and some of its many possible generalizations. Attribute exploration is useful for acquiring structured knowledge through an interactive process, by asking queries to an expert. Generalizations that handle incomplete, faulty, or imprecise data are discussed, but the focus lies on knowledge ...
Added: September 3, 2016
Kuznetsov S., Kaytoue M., Belfodil A., , in : International Journal of General Systems. Issue 49.: [б.и.], 2020. P. 271-285.
Order and lattice theory provides convenient mathematical tools for pattern mining, in particular for condensed irredundant representations of pattern spaces and their efficient generation. Formal Concept Analysis (FCA) offers a generic framework, called pattern structures, to formalize many types of patterns, such as itemsets, intervals, graphs, and sequence sets. Moreover, FCA provides generic algorithms to generate irredundantly all ...
Added: October 29, 2020
Springer, 2021
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 ...
Added: July 10, 2021
Kaytoue M., Napoli A., Kuznetsov S. et al., Information Sciences 2011 Vol. 181 No. 10 P. 1989-2001
This paper addresses the important problem of efficiently mining numerical data with formal concept analysis (FCA). Classically, the only way to apply FCA is to binarize the data, thanks to a so-called scaling procedure. This may either involve loss of information, or produce large and dense binary data known as hard to process. In the ...
Added: January 9, 2013
Ignatov D. I., , in : LNAI 14133: 28th International Conference on Conceptual Structures, ICCS 2023, Berlin, Germany, September 11–13, 2023, Proceedings. Graph-Based Representation and Reasoning. : Berlin : Springer, 2023. P. 56-69.
Set partitions and partition lattices are well-known objects in combinatorics and play an important role as a search space in many applied problems including ensemble clustering. Searching for antichains in such lattices is similar to that of in Boolean lattices. Counting the number of antichains in Boolean lattices is known as the Dedekind problem. In ...
Added: November 23, 2023
Kryszkiewicz M., Obiedkov Sergei, Ras Z., Fundamenta Informaticae 2012 Vol. 115 No. 4 P. i-ii
The paper is the preface to the special issue of the Fundamenta Informaticae journal on concept lattices and their applications. It is focused on recent developments in Formal Concept Analysis (FCA), as well as on applications in closely related areas such as data mining, information retrieval, knowledge management, data and knowledge engineering, and lattice theory. ...
Added: January 25, 2013
Ignatov D. I., Kuznetsov S., Zhukov L. E., , in : Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, June 25-27, 2011. Proceedings. Vol. 6743.: Berlin, Heidelberg : Springer, 2011. P. 257-264.
A novel approach to triclustering of a three-way binary data is proposed. Tricluster is defined in terms of Triadic Formal Concept Analysis as a dense triset of a binary relation Y , describing relationship between objects, attributes and conditions. This definition is a relaxation of a triconcept notion and makes it possible to find all ...
Added: December 3, 2012
Korepanova N., Kuznetsov S., , in : Formal Concept Analysis for Knowledge Discovery. Proceedings of International Workshop on Formal Concept Analysis for Knowledge Discovery (FCA4KD 2017), Moscow, Russia, June 1, 2017. Vol. 1921.: CEUR-WS.org, 2017. P. 13-21.
Today personalized medicine is one of the most popular interdisciplinary research field, risk group identification being one of its most important tasks. Even though the first attempts to estimate the effect of patient’s characteristics on the outcome were proposed in statistics in the middle of the twentieth century, it is still an open question how ...
Added: October 4, 2017
Kashnitsky Y., Kuznetsov S., , in : Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at ECAI 2016). : M. : [б.и.], 2016. P. 105-112.
Decision tree learning is one of the most popular classifica- tion techniques. However, by its nature it is a greedy approach to finding a classification hypothesis that optimizes some information-based crite- rion. It is very fast but may lead to finding suboptimal classification hy- potheses. Moreover, in spite of decision trees being easily interpretable, ensembles ...
Added: October 6, 2016
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
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
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
Kuznetsov S., Poelman J., Elzinga P. et al., Lecture Notes in Computer Science 2012 Vol. 7377 LNAI P. 528-272
In this paper we introduce a novel human-centered data mining software system which was designed to gain intelligence from unstructured textual data. The architecture takes its roots in several case studies which were a collaboration between the Amsterdam-Amstelland Police, GasthuisZusters Antwerpen (GZA) hospitals and KU Leuven. It is currently being implemented by bachelor and master ...
Added: February 7, 2013
Muratova A., Gizdatullin D., Ignatov D. I. et al., В кн. : Социология и общество: социальное неравенство и социальная справедливость (Екатеринбург , 19-21 октября 2016 года). Материалы V Всероссийского социологического конгресса. : М. : Российское общество социологов, 2016. С. 9601-9615.
In this paper, we summarize the results of recent studies on the application of pattern mining and machine learning to the analysis of demographic sequences. The main goal is the demonstration of demographers’ needs, including next-event prediction and the extraction of interesting patterns from substantial datasets of demographic data, which cannot be handled by conventional ...
Added: November 24, 2016
Cham : Springer, 2017
This book constitutes the proceedings of the 14th International Conference on Formal Concept Analysis, ICFCA 2017, held in Rennes, France, in June 2017. The 13 full papers presented in this volume were carefully reviewed and selected from 37 submissions. The book also contains an invited contribution and a historical paper translated from German and originally ...
Added: June 25, 2017
Yarullin R., Obiedkov S., International Journal of Approximate Reasoning 2020 Vol. 127 P. 1-16
In Angluin's exact-learning framework, equivalence queries can be simulated by stochastic equivalence testing to achieve a probably approximately correct identification of an unknown concept. We present an analysis of the number of samples that need to be generated in the process leading to a theoretical improvement on an earlier approach. We apply this modification to ...
Added: October 6, 2020
M. : Higher School of Economics, National Research University, 2016
The 13th International Conference on “Concept Lattices and Applications (CLA 2016)” was held at National Research University Higher School of Economics, Moscow, Russia from July 18 until July 22, 2016. The CLA conference, organized since 2002, aims to provide to everyone interested in Formal Concept Analysis and more generally in Concept Lattices or Galois Lattices, ...
Added: October 6, 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
Ignatov D. I., Watson B., , in : RuZA 2015 Workshop. Proceedings of Russian and South African Workshop on Knowledge Discovery Techniques Based on Formal Concept Analysis (RuZA 2015). November 30 - December 5, 2015, Stellenbosch, South Africa. Vol. 1552.: Aachen : CEUR Workshop Proceedings, 2015.
Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used ...
Added: June 14, 2016
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