Knowledge discovery from sequential data

@inproceedings{Hppner2003KnowledgeDF,
  title={Knowledge discovery from sequential data},
  author={Frank H{\"o}ppner},
  year={2003}
}
A new framework for analyzing sequential or temporal data such as time series is proposed. It differs from other approaches by the special emphasis on the interpretability of the results, since interpretability is of vital importance for knowledge discovery, that is, the development of new knowledge (in the head of a human) from a list of discovered patterns. While traditional approaches try to model and predict all time series observations, the focus in this work is on modelling local… CONTINUE READING
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