2013 IEEE 13th International Conference on Data Mining Workshops
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.
Развитие лингвистических технологий и распространение социальных медиа предоставляют мощные возможности для изучения настроений и психологических состояний пользователей интернета. В статье мы обсуждаем возможность исользования данных об эмоциональных состояниях пользователей Твиттера для повышения точности прогноза цен фондового рынка. В статье рассматривается применение словарного подхода для определения тональности сообщения по восьми базовым эмоциям и попытка использовать результаты аналаза более 755 миллиона сообщений в Твиттер для повышения точности прогноза фондового рынка. Обсуждается возможность использования метода опорных векторов и нейронных сетей для предсказания индексов DJIA и S&P500.
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 in specific games – conditions, which are rarely given. Contemporary experimental economics offers a number of alternative models apart from game theory. In relevant literature, these models are always biased by philosophical plausibility considerations and are claimed to fit the data. An agnostic data mining approach to the problem is introduced in this paper – the philosophical plausibility considerations follow after the correlations are found. No other biases are regarded apart from determinism. The dataset of the paper “Social Learning in Networks” by Choi et al 2012 is taken for evaluation. As a result, we come up with new findings. As future work, the design of a new infrastructure is discussed.