Warped K-Means: An algorithm to cluster sequentially-distributed data

  title={Warped K-Means: An algorithm to cluster sequentially-distributed data},
  author={Luis A. Leiva and Enrique Vidal},
  journal={Inf. Sci.},
Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the wellknown K-means algorithm and provide a general method to properly cluster… CONTINUE READING
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Mining the browsing context: Discovering interaction profiles via behavioral clustering. In: Adjunct Proceedings of the 19th conference on User Modeling, Adaptation, and Personalization (UMAP)

  • L. A. Leiva
  • 2011
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