Abstract
Discrete sequence data can be considered the categorical analog of time series data. As in the case of time series data, it contains a single contextual attribute that typically corresponds to time. However, the behavioral attribute is categorical.
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The original CLUSEQ algorithm also adjusts the similarity threshold \(t\) iteratively to optimize results.
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The assumption is that the initial set of state probabilities are approximately consistent with the steady state behavior of the model for the particular set of transition probabilities shown in Fig. 15.6.
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HMMs can also generate continuous time series, though they are less commonly used in timeseries analysis.
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© 2015 Springer International Publishing Switzerland
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Aggarwal, C. (2015). Mining Discrete Sequences. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_15
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DOI: https://doi.org/10.1007/978-3-319-14142-8_15
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