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T-Scroll: Visualizing Trends in a Time-Series of Documents for Interactive User Exploration

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Research and Advanced Technology for Digital Libraries (ECDL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4675))

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Abstract

On the Internet, a large number of documents such as news articles and online journals are delivered everyday. We often have to review major topics and topic transitions from a large time-series of documents, but it requires much time and effort to browse and analyze the target documents. We have therefore developed an information visualization system called T-Scroll (Trend/Topic-Scroll) to visualize the transition of topics extracted from those documents. The system takes periodical outputs of the underlying clustering system for a time-series of documents then visualizes the relationships between clusters as a scroll. Using its interaction facility, users can grasp the topic transitions and the details of topics for the target time period. This paper describes the idea, the functions, the implementation, and the evaluation of the T-Scroll system.

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References

  1. Kontostathis, A., Galitsky, L.M., Pottenger, W.M., Roy, S., Phelps, D.J.: A survey of emerging trend detection in textual data mining. In: Berry, M.W. (ed.) Survey of Text Mining: Clustering, Classification, and Retrieval, pp. 185–224. Springer, Heidelberg (2003)

    Google Scholar 

  2. Allan, J. (ed.): Topic Detection and Tracking: Event-based Information Organization. Kluwer, Dordrecht (2002)

    MATH  Google Scholar 

  3. Müller, W., Schumann, H.: Visualization methods for time-dependent data: An overview. In: Proc. of 2003 Winter Simulation Conf., pp. 737–745 (2003)

    Google Scholar 

  4. Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: Visualizing thematic challenges in large document collections. IEEE Trans. on Visualization and Computer Graphics 8(1), 9–20 (2002)

    Article  Google Scholar 

  5. Swan, R., Allan, J.: Automatic generation of overview timelines. In: Proc. of ACM SIGIR, pp. 49–56 (2000)

    Google Scholar 

  6. Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In: Proc. of ACM KDD, pp. 198–207 (2005)

    Google Scholar 

  7. Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: MONIC: Modeling and monitoring cluster transitions. In: Proc. of ACM KDD, pp. 706–711 (2006)

    Google Scholar 

  8. Ishikawa, Y., Chen, Y., Kitagawa, H.: An on-line document clustering method based on forgetting factors. In: Constantopoulos, P., Sølvberg, I.T. (eds.) ECDL 2001. LNCS, vol. 2163, pp. 332–339. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  9. Khy, S., Ishikawa, Y., Kitagawa, H.: Novelty-based incremental document clustering for on-line documents. In: WIRI 2006. Proc. of International Workshop on Challenges in Web Information Retrieval and Integration (2006)

    Google Scholar 

  10. Khy, S., Ishikawa, Y., Kitagawa, H.: A novelty-based clustering method for on-line documents. World Wide Web Journal (in press, 2007)

    Google Scholar 

  11. Egghe, L., Rousseau, R.: Introduction to Informetrics: Quantitative Methods in Library, Documentation and Information Science. Elsevier, Amsterdam (1990)

    Google Scholar 

  12. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    Google Scholar 

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László Kovács Norbert Fuhr Carlo Meghini

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Ishikawa, Y., Hasegawa, M. (2007). T-Scroll: Visualizing Trends in a Time-Series of Documents for Interactive User Exploration. In: Kovács, L., Fuhr, N., Meghini, C. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2007. Lecture Notes in Computer Science, vol 4675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74851-9_20

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  • DOI: https://doi.org/10.1007/978-3-540-74851-9_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74850-2

  • Online ISBN: 978-3-540-74851-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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