Abstract
We address the problem of Topic Detection and Tracking (TDT) and subsequently detecting trends from a stream of text documents. Formulating TDT as a clustering problem in a class of self-organizing neural networks, we propose an incremental clustering algorithm. On this setup we show how trends can be identified. Through experimental studies, we observe that our method enables discovering interesting trends that are deducible only from reading all relevant documents.
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© 2001 Springer-Verlag Berlin Heidelberg
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Rajaraman, K., Tan, AH. (2001). Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_13
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DOI: https://doi.org/10.1007/3-540-45357-1_13
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