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Abstract: A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the... (Update)
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BibTeX entry: (Update)
J. Kleinberg. Bursty and hierarchical structure in streams. In the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,July 23 - 26, 2002. http://citeseer.ist.psu.edu/article/kleinberg02bursty.html More
@misc{ rg-bursty,
author = "Jon Kleinberg",
title = "Bursty and Hierarchical Structure in Streams",
url = "citeseer.ist.psu.edu/article/kleinberg02bursty.html" }
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The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.cornell.edu/home/kleinber/): More
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