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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Springer Book Archive