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News
May 22, 2026
HSE Graduates AI Project Wins at TECH & AI Awards
Daria Davydova, graduate of the HSE Graduate School of Business and Head of the AI Implementation Unit at the Artificial Intelligence Department of Alfa-Bank, received a prize at the TECH & AI Awards. She was awarded for the best AI solution for optimising business processes. The winners were determined as part of the VII Russian Summit and Awards on Digital Transformation (CDO/CDTO Summit & Awards).
May 20, 2026
HSE University Opens First Representative Office of Satellite Laboratory in Brazil
HSE University-St Petersburg opened a representative office of the Satellite Laboratory on Social Entrepreneurship at the University of Campinas in Brazil. The platform is going to unite research and educational projects in the spheres of sustainable development, communications and social innovations.
May 18, 2026
The 'Second Shift' Is Not Why Women Avoid News
Women are more likely than men to avoid political and economic news, but the reasons for this behaviour are linked less to structural inequality or family-related stress than to personal attitudes and the emotional perception of news content. This conclusion was reached by HSE researchers after analysing data from a large-scale survey of more than 10,000 residents across 61 regions of Russia. The study findings have been published in Woman in Russian Society.

 

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2013 IEEE 13th International Conference on Data Mining Workshops

Los Alamitos : IEEE Computer Society, 2013.
Academic editor: W. Ding, T. Washio, H. Xiong, G. Karypis, B. Thuraisingham, D. Cook, X. Wu
Compiler: L. O’Conner

The 13rd IEEE International Conference on Data Mining (IEEE ICDM 2013) has solicited workshops on topics related to new research directions and novel applications of data mining. The goal of the ICDM workshops program (IEEE ICDMW) is to identify grand challenges in data mining, to explore the possible paths to address these urgent problems, and to solicit broad participation from the data mining community and other relevant research communities. IEEE ICDMW 2013 was held on December 7 in Dallas, Texas, USA, and was immediately followed by IEEE ICDM 2013. This year, we have received 41 workshop proposals, a 141% increase from the number of proposals in the previous year. Of those submissions, 26 workshop proposals were accepted through a thorough review by the ICDMW workshop organization committee. 18 workshops eventually made their way to prepare their workshop programs after a rigorous paper review process. The final program consisted of 13 full-day workshops and 5 halfday workshops. Overall, the ICDMW Program received 364 submissions, which is a 19% increase from the number of submissions in the previous year. Of those submissions, 183 papers were accepted. The workshop proposal acceptance rate is about 44%, and the workshop papers acceptance rate is about 50%. The highly competitive acceptance rates have resulted in the highquality and exciting ICDMW proceedings. IEEE ICDMW 2013 covered many new research and application areas as well as fundamental data mining topics. The traditional and fundamental disciplines included spatial and spatiotemporal data mining, optimization, concept drift, domain driven data mining, opinion mining, and sentiment analysis. Emerging disciplines included high-dimensional data mining, causal discovery, cloud and distributed computing, data mining in service applications, and of course, big data. IEEE ICDMW 2013 provided discussion forums for exciting applications including biological data mining in healthcare, data mining in networks, data privacy, and data mining case studies. The ICDMW Program also explored new areas of data markets in sciences and businesses, data mining in experimental economics, and data mining in astronomical problems. Many people worked together in organizing IEEE ICDMW 2013. We would like to thank all workshop organizers for the high-quality workshop proposals received. The workshop organizers are the key to the success of the ICDMW program. We should thank them all for their tremendous effort putting together 18 exciting workshops in the final program.

Chapters
Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis.
Porshnev A., Redkin I. E., Shevchenko A. V., , in: 2013 IEEE 13th International Conference on Data Mining Workshops.: Los Alamitos: IEEE Computer Society, 2013. Ch. 9.3 P. 440–444.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow ...
Added: February 20, 2014
Social Learning in Networks: Extraction of Deterministic Rules
Tagiew R., Ignatov D. I., Amroush F., , in: 2013 IEEE 13th International Conference on Data Mining Workshops.: Los Alamitos: IEEE Computer Society, 2013. P. 445–451.
In this paper, we want to introduce experimental economics to the field of data mining and vice versa. It continues related work on mining deterministic behavior rules of human subjects in data gathered from experiments. Game-theoretic predictions partially fail to work with this data. Equilibria also known as game-theoretic predictions solely succeed with experienced subjects ...
Added: June 9, 2014
The Early Booking Effect and Other Determinants of Hotel Room Prices in Europe
Bezzubtseva A. A., Ignatov D. I., , in: 2013 IEEE 13th International Conference on Data Mining Workshops.: Los Alamitos: IEEE Computer Society, 2013. P. 426–432.
This paper presents a preliminary analysis of hotel room prices in several European cities based on the data from Booking.com website. The main question raised in the study is whether early booking is advantageous indeed, and if so, how early should it be? First a script was developed to download more than 600 thousand hotel ...
Added: June 9, 2014
Research target: Computer Science
Priority areas: economics IT and mathematics business informatics
Language: English
Text on another site
Keywords: data miningинтеллектуальный анализ данных
2013 IEEE 13th International Conference on Data Mining Workshops
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