J. Kleinberg. "Bursty and Hierarchical Structure in Streams." Proc. 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2002. Home/Search Document Details and Download
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....di#erent sliding window size Figure 8: Bursts in population distribution data for di#erent spatial sliding window sizes As an emotive example, we monitor bursts of interest in countries from the presidential State of the Union addresses from 1913 to 2003. The same example was used by Kleinberg [16] to show the bursty structure of text streams. In figure 6 we show the number of times that some countries were mentioned in the speeches. There are clearly bursts of interest in certain countries. An interesting observation here is that these bursts have di#erent durations, varying from years to ....
....search from time series[2] Data mining on bursty behavior has attracted more attention recently. Wang et al. 21] study fast algorithms using self similarity to model bursty time series. Such models can generate more realistic time series streams for testing data mining algorithm. Kleinberg[16] also discusses the problem of burst detection in data streams. The focus of his work is in modeling and extracting structure from text streams. Our work is di#erent in that we focus on the algorithmic issue of counting over di#erent sliding windows. We have extended the data structure for burst ....
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