%0 Journal Article %1 kleinberg2003bursty %A Kleinberg, Jon %D 2003 %J Data Mining and Knowledge Discovery %K analysis burst hierarchy stream structure temporal time web %N 4 %P 373--397 %R 10.1023/A:1024940629314 %T Bursty and Hierarchical Structure in Streams %U https://doi.org/10.1023/A:1024940629314 %V 7 %X 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 following intuitive premise---that the appearance of a topic in a document stream is signaled by a ``burst of activity,'' with certain features rising sharply in frequency as the topic emerges.

@article{kleinberg2003bursty, 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 following intuitive premise---that the appearance of a topic in a document stream is signaled by a ``burst of activity,'' with certain features rising sharply in frequency as the topic emerges.}, added-at = {2021-01-20T09:38:35.000+0100}, author = {Kleinberg, Jon}, biburl = {https://www.bibsonomy.org/bibtex/2162eb7b3982f6edd7a1322c661d7bed7/jaeschke}, day = 01, description = {Bursty and Hierarchical Structure in Streams | SpringerLink}, doi = {10.1023/A:1024940629314}, interhash = {85f02e6bdb8c5c56b7e71d4eb63bf855}, intrahash = {162eb7b3982f6edd7a1322c661d7bed7}, issn = {1573-756X}, journal = {Data Mining and Knowledge Discovery}, keywords = {analysis burst hierarchy stream structure temporal time web}, month = oct, number = 4, pages = {373--397}, timestamp = {2021-01-20T09:38:35.000+0100}, title = {Bursty and Hierarchical Structure in Streams}, url = {https://doi.org/10.1023/A:1024940629314}, volume = 7, year = 2003 }