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SOLUTION TO PROBLEM OF IDENTIFYING HIGH-LEVEL CHARACTERISTICS OF USSD-MENU USERS BASING ON LOW-LEVEL CHARACTERISTICS BY APPLYING FORMAL NOTIONS ANALYSIS
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Ekaterina Vladimirovna Shadrina
Novosibirsk National Research State University
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Submitted:
November 17, 2014
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Abstract.
The article considers the experience and prospects of applying methods on the basis of formal notions analysis to the data processing of social networks users for the subsequent transfer of this experience to the analysis of the logs of USSD-services users. The article provides an overview of existing solutions for social networks, identifies the prospects of applying formal notions analysis to the solution to the task of distinguishing the high-level characteristics of USSD-services users on the basis of low-level characteristics.
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Key words and phrases:
USSD
социальная сеть
анализ формальных понятий
высокоуровневая характеристика
низкоуровневая характеристика
social network
formal notions analysis
high-level characteristics
low-level characteristics
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References:
- Гнатышак Д. В., Игнатов Д. И. Анализ данных (data mining) онлайн социальных сетей с помощью бикластеризации и трикластеризации. М., 2010.
- Игнатов Д. И. Методы бикластеризации для анализа интернет-данных. М., 2008.
- Пальчунов Д. Е. Моделирование мышления и формализация рефлексии. Ч. 2. Онтологии и формализация понятий // Философия науки. 2008. № 2 (37). С. 62-99.
- Шадрина Е. В. Имитационный генератор логов обращений к USSD-меню. Новосибирск, 2011.
- Шадрина Е. В. Методы статистической обработки и генерации логов обращений к USSD-меню. Новосибирск, 2013.
- Andrews P. S. Visualising Computational Intelligence through Converting Data into Formal Concepts // Next Generation Data Technologies for Collective Computational Intelligence. Sheffield, 2011.
- Benedicte Le Grand A. A. Advances in FCA-Based Applications for Social Networks Analysis. Paris: Sorbonne University, 2013.
- Bentayeb Selmane S. A., Boussaid O., Missaoui F. Mining Triadic Association Rules. Gatineau, 2011.
- Codocedo L. I. Semantic Querying of Data Guided by Formal Concept Analysis. Luxembourg, 2012.
- Cuvelier M. A Buzz and E-Reputation Monitoring Tool for Twitter Based on Galois Lattices. Derby, 2011.
- Freeman L. C. The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver, 2004.
- Ganter B. Formal Concept Analysis: Foundations and Applications. Berlin, 2005.
- Ignatov D. I., Kuznetsov S. O. From Triconcepts to Triclusters // Proceedings of the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Moscow, 2011.
- Missaoui R. Social Network Analysis Using Formal Concept Analysis. Ottawa, 2013.
- Palchunov D. E. Lattices of Relatively Axiomatizable Classes // Lecture Notes in Artificial Intelligence. Berlin - Heidelberg - Clermont-Ferrand, 2007. P. 221-239.
- Palchunov D. E. Virtual Catalog: the Ontology-Based Technology for Information Retrieval // Lecture Notes in Artificial Intelligence. Berlin - Heidelberg - Novosibirsk, 2011. P. 164-183.
- Riadh T. M. Powerconcept: Conceptual Metrics’ Distributed Computation // 8th International Conference on Formal Concept Analysis. Morocco, 2010.
- Rokia Missaoui L. K. Mining Triadic Association Rules from Ternary Relations // Formal Concept Analysis. Lecture Notes in Computer Science. Berlin - Heidelberg, 2011.
- Scott J. Social Network Analysis. L., 2000.
- Wal V. Folksonomy Coinage and Definition [Электронный ресурс]. URL: http://vanderwal.net/folksonomy.html (дата обращения: 24.09.2014).
- Wille R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. Darmstadt, 1982.
- Wolff K. E., Palchunov D. E. Knowledge Processing and Data Analysis // Lecture Notes in Artificial Intelligence. Berlin - Heidelberg - Novosibirsk, 2011.
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