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Разработка данных систем совместного пользования ресурсами: от трипонятий к трикластерам
С. 258-261.
Kashnitsky Y., В кн. : Труды Международной конференции по физико-технической информатике CPT-2013, 12-19 мая 2013 г., Ларнака, Республика Кипр. : М., Протвино : Изд-во ИФТИ, 2013. С. 251-258.
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 similar ...
Added: January 27, 2014
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
Ignatov D. I., Kuznetsov S., Zhukov L. E. et al., International Journal of General Systems 2013 Vol. 42 No. 6 P. 572-593
formal concept analysis,
data mining,
triclustering,
three-way data,
folksonomy,
spectral triclustering ...
Added: October 16, 2013
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., Zhuk R., Konstantinova N., , in : Proceedings of The 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2014, 11-14 August 2014 Warsaw, Poland. : Los Alamitos, Washington, Tokyo : IEEE Computer Society, 2014. P. 474-480.
We propose extensions of the classical JSM-method andtheNa ̈ıveBayesianclassifierforthecaseoftriadicrelational data. We performed a series of experiments on various types of data (both real and synthetic) to estimate quality of classification techniques and compare them with other classification algorithms that generate hypotheses, e.g. ID3 and Random Forest. In addition to classification precision and recall we also ...
Added: June 9, 2014
Ignatov D. I., Semenov A., Комиссарова Д. В. et al., , in : Formal Concept Analysis of Social Networks. : Springer, 2017. Ch. 4. P. 59-96.
Multimodal clustering is an unsupervised technique for mining interesting patterns in n-ary relations or n-mode networks. Among different types of such generalised patterns one can find biclusters and formal concepts (maximal bicliques) for two-mode case, triclusters and triconcepts for three-mode case, closed n-sets for n-mode case, etc. Object-attribute biclustering (OA-biclustering) for mining large binary datatables (formal contexts or two-mode ...
Added: December 17, 2017
Kaytoue M., Kuznetsov S., Macko J. et al., Annals of Mathematics and Artificial Intelligence 2014 Vol. 70 No. 1 P. 55-79
Biclustering numerical data became a popular data-mining task at the beginning of 2000’s, especially for gene expression data analysis and recommender systems. A bicluster reflects a strong association between a subset of objects and a subset of attributes in a numerical object/attribute data-table. So-called biclusters of similar values can be thought as maximal sub-tables with ...
Added: October 27, 2015
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
Gnatyshak D. V., Ignatov D. I., Semenov A. et al., , in : Perspectives in Business Informatics Research. 11th International Conference, BIR 2012, Nizhny Novgorod, Russia, September 2012 Proceedings. Issue 128.: Berlin, Heidelberg : Springer, 2012. P. 162-171.
We combine bi- and triclustering to analyse data collected from the Russian online social network Vkontakte. Using biclustering we extract groups of users with similar interests and find communities of users which belong to similar groups. With triclustering we reveal users' interests as tags and use them to describe Vkontakte groups. After this social tagging ...
Added: December 3, 2012
Ignatov D. I., Gnatyshak D. V., Sergei O. Kuznetsov et al., Machine Learning 2015 Vol. 101 No. 1 P. 271-302
This paper presents several definitions of “optimal patterns” in triadic data and results of experimental comparison of five triclustering algorithms on real-world and synthetic datasets. The evaluation is carried over such criteria as resource efficiency, noise tolerance and quality scores involving cardinality, density, coverage, and diversity of the patterns. An ideal triadic pattern is a totally dense ...
Added: April 15, 2015
Gnatyshak D. V., Ignatov D. I., Semenov A. et al., , in : Concept Discovery in Unstructured Data. 2nd International Workshop, CDUD 2012, Leuven, Belgium, May 2012, Proceedings. Issue 871.: Leuven : Katholieke Universiteit Leuven, 2012. P. 30-39.
In this paper we propose two novel methods for analyzing data collected from online social networks. In particular we will do analyses on Vkontake data (Russian online social network). Using biclustering we extract groups of users with similar interests and find communities of users which belong to similar groups. With triclustering we reveal users’ interests ...
Added: November 20, 2012
Гнатышак Д. В., Научно-техническая информация. Серия 2: Информационные процессы и системы 2015 № 2 С. 16-30
В связи с продолжающимся ростом популярности области больших данных все более активно ставится вопрос о создании эффективных алгоритмов с низкой временной сложностью и возможностью параллелизации. Целью данной работы было создание эффективного однопроходного алгоритма трикластеризации бинарных данных, пригодного для использования в области больших данных. В результате был получен однопроходный линейный онлайн-алгоритм OAC-трикластеризации (трикластеризации объект-признак-условие). Помимо того, ...
Added: April 15, 2015
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
Springer, 2017
The book studies the existing and potential connections between Social Network Analysis (SNA) and Formal Concept Analysis (FCA) by showing how standard SNA techniques, usually based on graph theory, can be supplemented by FCA methods, which rely on lattice theory.
The book presents contributions to the following areas: acquisition of terminological knowledge from social networks, knowledge ...
Added: December 17, 2017
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
Poelmans J., Ignatov D. I., Инженерия знаний и технологии семантического веба 2011 № 2 С. 9-18
...
Added: September 24, 2012
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
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., 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
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
Ayzenberg A., / Cornell University. Series arXiv "math". 2019.
The general goal of this paper is to gather and review several methods from homotopy and combinatorial topology and formal concepts analysis (FCA) and analyze their connections. FCA appears naturally in the problem of combinatorial simplification of simplicial complexes and allows to see a certain duality on a class of simplicial complexes. This duality generalizes ...
Added: November 15, 2019