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Analyzing Social Networks Services Using FormalConcept Analysis Research Toolbox
Ch. 5. P. 43-54.
Nowadays social data analysts use a complicated mix of languages, methods and technologies for analyzing social networks services (SNS) data. In this article we describe approaches and technologies for extracting, analyzing and visualizing social data using Formal Concept Analysis Research Toolbox (FCART). Integrated process of analyzing SNS data with a set of research tools based on Formal Concept Analysis is considered with examples on datasets from Russian segment of LiveJournal.
Keywords: data miningknowledge discoveryFCA (Formal Concept Analysis)applied software systemssocial network analysis
Publication based on the results of:
In book
Issue 1534: SNAFCA 2015 Social Network Analysis using Formal Concept Analysis. , Malaga : CEUR Workshop Proceedings, 2015
Parinov A., Neznanov A., , in : CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings. Vol. 1624.: M. : Higher School of Economics, National Research University, 2016. P. 285-296.
Formal Concept Analysis (FCA) provides mathematical models, methods and algorithms for data analysis. However, by now there is no easily available program system, which would provide data analyst with unified, intelligible and transparent access to various external data sources with large amount of heterogeneous data for subsequent FCA-based knowledge discovery. The lack of such tools ...
Added: October 19, 2016
Cham : Springer, 2022
This book constitutes revised selected papers from the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held during December 16-18, 2021. The world of Data Science changes every year. At AIST, we exchange our understanding of the Science state-of-the-art, as well as how it applies to life and business. AIST ...
Added: January 4, 2022
Berlin : Springer, 2014
This book constitutes the proceedings of the Third International Conference on Analysis of Images, Social Networks and Texts, AIST 2014, held in Yekaterinburg, Russia, in April 2014. The 11 full and 10 short papers were carefully reviewed and selected from 74 submissions. They are presented together with 3 short industrial papers, 4 invited papers and ...
Added: November 13, 2014
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
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
Ignatov D. I., Kuznetsov S., Poelmans J., Leuven : Katholieke Universiteit Leuven, 2012
Added: November 20, 2012
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
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
Neznanov A., Parinov A., , in : Intelligent Distributed Computing IX. : Springer, 2015. P. 265-271.
This paper describes distributed architecture and data workflow of the analysis system called FCART. Comparing with the similar systems FCART is capable of dealing with various data sources, data preprocessing and interactive analysis, extending functionality by integrating independent web-services and developing plugins. Example of gathering and analyzing data of social networking service is considered. ...
Added: October 19, 2015
Semenov A., Natekin A., Nikolenko S. I. et al., Springer, 2015
In online social networks, high level features of user behavior such as character traits can be predicted with data from user profiles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis. In this study, we investigate the capabilities of previously published methods and metrics applied to the Russian online social ...
Added: December 21, 2015
Springer, 2015
Proceedings of the 9th International Symposium on Intelligent Distributed Computing – IDC'2015, Guimarães, Portugal, October 2015 ...
Added: October 19, 2015
Springer, 2014
This book constitutes the refereed proceedings of the 10th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2014, held in St. Petersburg, Russia in July 2014. The 40 full papers presented were carefully reviewed and selected from 128 submissions. The topics range from theoretical topics for classification, clustering, association rule and ...
Added: September 30, 2014
CEUR Workshop Proceedings, 2016
This volume contains the papers presented at the Second International Workshop on Soft Computing Applications and Knowledge Discovery (SCAKD 2016) held on July 18, 2016 at the National Research University Higher School of Economics, Moscow, Russia. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to ...
Added: September 28, 2016
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
Semenov A., Natekin A., Nikolenko S. I. et al., , in : Analysis of Images, Social Networks and Texts. 4th International Conference, AIST 2015, Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers. Vol. 542: Series: Communications in Computer and Information Science.: Switzerland : Springer, 2015. Ch. 3. P. 24-35.
In online social networks, high level features of user behavior such as character traits can be predicted with data from user profiles and their connections. Recent publications use data from online social networks to detect people with depression propensity and diagnosis. In this study, we investigate the capabilities of previously published methods and metrics applied ...
Added: October 28, 2015
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
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., 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
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
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
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
M. : Higher School of Economics Publishing House, 2011
Added: August 31, 2012
Leuven : Katholieke Universiteit Leuven, 2012
Concept discovery is a subarea of Knowledge Discovery in Databases (KDD)
where concept models, such as Formal Concept Analysis (FCA), multimodal
clustering, conceptual graphs and other, are used for gaining insight into the
underlying conceptual structure of data. Traditional machine learning techniques
are mainly focusing on structured data given by object-attribute tables, whereas
most data available nowadays are given in ...
Added: March 10, 2013