Abstract
Data mining from time series documents is a new challenge in text mining and, for this purpose, time dependent feature extraction is an important problem. This paper proposes a method to track feature terms in time series documents. When analyzing and mining time series data, the key is to handle time information. The proposed method applies non-linear principal component analysis to document vectors that consist of term frequencies and time information. This paper reports preliminary experimental results in which the proposed method is applied to a corpus of topic detection and tracking, and we show that the proposed method is effective in extracting time dependent terms.
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References
Allan, J.: Topic Detection and Tracking: Event-based Infromation Organization. Kluwer Academic Publishers, Dordrecht (2002)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Infromation Retrieval. Addison-Wesley, Reading (1999)
Brants, T., Chen, F., Farahat, A.: A system for new event detection. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 330–337 (2003)
Deerwester, S., Dumais, S.T., Harshman, R.: Indexing by latent semantic analysis. Journal of American Society of Information Systems 41(2), 391–407 (1990)
Franz, M., Scott McCarley, J.: Unsupervised and supervised clustering for topic tracking. In: Proceedings of the 21th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 310–317 (2000)
Ishikawa, Y., Chen, Y., Kitagawa, H.: An on-line document clustering method based on forgetting factors. In: Proceedings of the 5th European Conference on Research and Advanced Technology for Digital Libraries, pp. 325–339 (2001)
Scholkopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Technical Report 44, Max-Plank- Institute fur Biolgische Kybernetik (1996)
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© 2004 Springer-Verlag Berlin Heidelberg
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Takasu, A., Tanaka, K. (2004). Feature Word Tracking in Time Series Documents. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_97
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DOI: https://doi.org/10.1007/978-3-540-28651-6_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
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