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

@article{Leiva2013WarpedKA,
  title={Warped K-Means: An algorithm to cluster sequentially-distributed data},
  author={Luis A. Leiva and Enrique Vidal},
  journal={Inf. Sci.},
  year={2013},
  volume={237},
  pages={196-210}
}
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
Highly Cited
This paper has 32 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 1 time over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 18 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 59 references

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
1 Excerpt

Similar Papers

Loading similar papers…