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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 871412, 11 pages
http://dx.doi.org/10.1155/2014/871412
Research Article

An Activation Method of Topic Dictionary to Expand Training Data for Trend Rule Discovery

IT Research and Development Center, Toshiba Solutions Corporation, 3-22 Katamachi, Fuchu, Tokyo 183-8512, Japan

Received 23 August 2013; Revised 28 December 2013; Accepted 13 January 2014; Published 26 February 2014

Academic Editor: Ying-Tung Hsiao

Copyright © 2014 Shigeaki Sakurai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. Agrawal and R. Srikant, “Mining sequential patterns,” in Proceedings of the 1995 International Conference on Knowledge Discovery and Data Mining, pp. 3–14, March 1995. View at Scopus
  2. J. Pei, J. Han, B. Mortazavi-Asl et al., “PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth,” in Proceedings of the 17th International Conference on Data Engineering, pp. 215–224, April 2001. View at Scopus
  3. S. Sakurai and R. Orihara, “Discovery of important threads from bulletin board sites,” International Journal of Information Technology and Intelligent Computing, vol. 1, no. 1, pp. 217–228, 2006.
  4. S. Sakurai and K. Ueno, “Analysis of daily business reports based on sequential text mining method,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '04), vol. 4, pp. 3279–3284, October 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Yen, “Mining interesting sequential patterns for intelligent systems,” International Journal of Intelligent Systems, vol. 20, no. 1, pp. 73–87, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Sakurai, K. Makino, and S. Matsumoto, “A discovery method of trend rules from complex sequential data,” in Proceedings of the 26th IEEE International Conference on Advanced Information Networking and Applications Workshops (AINA '12), pp. 950–955, 2012.
  7. W. Antweiler and M. Z. Frank, “Is all that talk just noise? The information content of Internet stock message boards,” Journal of Finance, vol. 59, no. 3, pp. 1259–1294, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Bollen, H. Mao, and X. Zeng, “Twitter mood predicts the stock market,” October 2010, http://arxiv.org/PS_cache/arxiv/pdf/1010/1010.3003v1.pdf.
  9. X. Zhang, H. Fuehres, and P. A. Gloor, “Predicting Stock Market Indicators through Twitter, ‘I hope it is not as bad as I fear’,” Procedia, vol. 26, pp. 55–62, 2011.
  10. G. P. C. Fung, J. X. Yu, and W. Lam, “News sensitive stock trend prediction,” in Proceedngs of the 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 481–493, 2002.
  11. M. Mittermayer and G. F. Knolmayer, “NewsCATS: a news categorization and trading system,” in Proceedings of the 6th International Conference on Data Mining, pp. 1002–1007, December 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Peramunetilleke and R. K. Wong, “Currency exchange rate forecasting from news headlines,” in Proceedings of the 13th Australasian Database Conference, vol. 5, pp. 131–139, 2002.
  13. M. de Choudhury, H. Sundaram, A. John, and D. D. Seligmann, “Can blog communication dynamics be correlated with stock market activity?” in Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (HT '08), pp. 55–60, June 2008. View at Scopus
  14. Y. Seo, J. A. Giampapa, and K. P. Sycaratech, “Financial news analysis for intelligent portfolio management,” Report CMU-RI-TR-04-04, Robotics Institute, Carnegie Mellon University, 2004.
  15. S. Sakurai, K. Makino, H. Suzuki, and Y. Masaoka, “Ranking of evaluation targets based on complex sequential data,” in Proceedings of the 25th Annual Conference of the Japanese Society for Artificial Intelligence, 2G2-01, 2011, (Japanese).
  16. Chasen, 2010 (Japanese), http://chasen.naist.jp/hiki/ChaSen/.
  17. J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, vol. 29, no. 2, pp. 1–12, 2000. View at Scopus