Sequential Document Visualization

  title={Sequential Document Visualization},
  author={Yi Mao and Joshua V. Dillon and Guy Lebanon},
  journal={IEEE Transactions on Visualization and Computer Graphics},
Documents and other categorical valued time series are often characterized by the frequencies of short range sequential patterns such as n-grams. This representation converts sequential data of varying lengths to high dimensional histogram vectors which are easily modeled by standard statistical models. Unfortunately, the histogram representation ignores most of the medium and long range sequential dependencies making it unsuitable for visualizing sequential data. We present a novel framework… CONTINUE READING
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