?
Система анализа данных коллаборативных платформ CrowDM
С. 16-26.
In book
Вып. 1. , М. : Национальный открытый университет «ИНТУИТ», 2012
Ignatov D. I., Kaminskaya A. Y., Bezzubtseva A. A. et al., , in : Перспективные направления исследований в области бизнес-информатики: Материалы XI международной конференции. : Nizhny Novgorod : Higher School of Economics in Nizhny Novgorod, 2012. P. 7-17.
In a crowdsourcing project several participants discuss and solve one common problem, propose their ideas, evaluate ideas of each other, etc. We propose the novel instrument CrowDM for analyzing data generated by collaborative platforms. The initial version of the system combines several innovative techniques for structured and unstructured data analysis. Formal Concept Analysis, multimodal clustering ...
Added: December 3, 2012
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
Ignatov D. I., Kaminskaya A. Y., Malioukov A. et al., , in : Proceedings of International Conference on Conceptual Structures 2014. Vol. 8577: Graph-Based Representation and Reasoning.: Springer, 2014. P. 287-292.
This paper considers a recommender part of the data anal- ysis system for the collaborative platform Witology. It was developed by the joint research team of the National Research University Higher School of Economics and the Witology company. This recommender sys- tem is able to recommend ideas, like-minded users and antagonists at the respective phases ...
Added: June 9, 2014
M. : Higher School of Economics Publishing House, 2011
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often ...
Added: December 3, 2012
Ignatov D. I., Kuznetsov S., Poelmans J., Leuven : Katholieke Universiteit Leuven, 2012
Added: November 20, 2012
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
Ignatov D. I., В кн. : Анализ изображений, сетей и текстов. Доклады всероссийской научной конференции АИСТ'12. Модели, алгоритмы и инструменты анализа данных; результаты и возможности для анализа изображений, сетей и текстов. Екатеринбург, 16 – 18 марта 2012 года. Вып. 1.: М. : Национальный открытый университет «ИНТУИТ», 2012. С. 3-15.
В работе даются основные определения анализа формальных понятий (АФП), рассказывается о его роли в математике и компьютерных науках, а также приводится краткий обзор его основных приложений. ...
Added: January 30, 2013
Leuven : Katholieke Universiteit Leuven, 2011
This book constitutes the second part of the refereed proceedings of the 10th International Conference on Formal Concept Analysis, ICFCA 2012, held in Leuven, Belgium in May 2012. The topics covered in this volume range from recent advances in machine learning and data mining; mining terrorist networks and revealing criminals; concept-based process mining; to scalability ...
Added: December 3, 2012
Egurnov D., Ignatov D. I., Точилкин Д. С., / Springer. Series LNCS "Lecture Notes in Computer Science". 2020.
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 the time and space complexity of ...
Added: November 10, 2020
Clermont-Ferrand : CEUR Workshop Proceedings, 2015
Formal Concept Analysis is a method of analysis of logical data based on formalization of conceptual knowledge by means of lattice theory. It has proved to be of interest to various applied fields such as data visualization, knowledge discovery and data mining, database theory, and many others. The International Conference “Concept Lattices and Their Applications ...
Added: October 22, 2015
University Rennes 1, 2017
This volume is the supplementary volume of the 14th International Conference on Formal Concept Analysis (ICFCA 2017), held from June 13th to 16th 2017, at IRISA, Rennes. The ICFCA conference series is one of the major venues for researches from the field of Formal Concept Analysis and related areas to present and discuss their recent ...
Added: June 19, 2017
CEUR-WS.org, 2020
The CLA conference is an international forum for researchers, practitioners and students dedicated to the practice of Formal Concept Analysis (FCA) and areas closely related to it, including data analysis and mining, information retrieval, knowledge management, knowledge engineering, logic, algebra and lattice theory.
The 15th of CLA, CLA 2020, was going to be held in Tallinn, Estonia ...
Added: October 30, 2020
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., , in : Conceptual Structures: Leveraging Semantic Technologies. 17th International Conference on Conceptual Structures, ICCS 2009, Moscow, Russia, July 26-31, 2009, Proceedings. Vol. 5662.: Berlin, Heidelberg : Springer, 2009. P. 185-200.
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a ...
Added: December 9, 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
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
Domenach F., Ignatov D. I., Poelmans J., Berlin, Heidelberg : Springer, 2012
This book constitutes the refereed proceedings of the 10th International Conference on Formal Concept Analysis, ICFCA 2012, held in Leuven, Belgium in May 2012. The 20 revised full papers presented together with 6 invited talks were carefully reviewed and selected from 68 submissions. The topics covered in this volume range from recent advances in machine ...
Added: December 3, 2012
Naidenova X., Ignatov D. I., Hershey : IGI Global, 2012
The consideration of symbolic machine learning algorithms as an entire class will make it possible, in the future, to generate algorithms, with the aid of some parameters, depending on the initial users’ requirements and the quality of solving targeted problems in domain applications.
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems surveys, analyzes, and ...
Added: December 3, 2012
Ignatov D. I., Kuznetsov S., , in : CLA 2008. Proceedings of the Sixth International Conference on Concept Lattices and Their Applications. : Olomouc : Palacky University, 2008. P. 157-166.
The problem of detecting terms that can be interesting to the advertiser is considered. If a company has already bought some advertising terms which describe certain services, it is reasonable to find out the terms bought by competing companies. A part of them can be recommended as future advertising terms to the company. The goal ...
Added: December 9, 2012
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., Kuznetsov S., В кн. : Двенадцатая национальная конференция по искусственному интеллекту с международным участием КИИ-2010 (20-24 сентября 2010 г., г. Тверь, Россия). Труды конференции. Том 1. Т. 1.: М. : Физматлит, 2010. С. 175-182.
В работе предлагается новый метод бикластеризации объектно-признаковых данных, опирающийся на свойства решеток замкнутых множеств. Предложено определение плотного бикластера, эффективный алгоритм для поиска таких бикластеров, исследована его сложность, проведены вычислительные эксперименты на реальных данных. Исследована на практике возможность масштабирования (распараллеливания) алгоритма. ...
Added: December 3, 2012
Springer, 2014
This book constitutes the proceedings of the 21st International Conference on Conceptual Structures, ICCS 2014, held in Iaşi, Romania, in July 2014. The 17 regular papers and 6 short papers presented in this volume were carefully reviewed and selected from 40 and 10 submissions, respectively. The topics covered are: conceptual structures, knowledge representation, reasoning, conceptual ...
Added: June 9, 2014
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
Kashnitsky Y., Ignatov D. I., Интеллектуальные системы. Теория и приложения 2015 Т. 19 № 4 С. 37-55
The paper makes a brief introduction into multiple classifier systems and describes a particular algorithm which improves classification accuracy by making a recommendation of an algorithm to an object. This recommendation is done under a hypothesis that a classifier is likely to predict the label of the object correctly if it has correctly classified its ...
Added: December 7, 2015