A Practical Tool for Visualizing and Data Mining Medical Time Series

  • Authors:

  • Nitin Kumar;Venkata Lolla;Eamonn Keogh;Chotirat Ann Ratanamahatana;Helga Van Herle

  • Affiliations:

  • University of California at Riverside;University of California at Riverside;University of California at Riverside;University of California at Riverside;University of California at Los Angeles

  • Venue:
  • CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
  • Year:
  • 2005

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Abstract

The increasing interest in time series data mining has had surprisingly little impact on real world medical applications. Practitioners who work with time series on a daily basis rarely take advantage of the wealth of tools that the data mining community has made available. In this work, we attempt to address this problem by introducing a parameter-light tool that allows users to efficiently navigate through large collections of time series. Our approach extracts features from a time series of arbitrary length and uses information about the relative frequency of these features to color a bitmap in a principled way. By visualizing the similarities and differences within a collection of bitmaps, a user can quickly discover clusters, anomalies, and other regularities within the data collection. We demonstrate the utility of our approach with a set of comprehensive experiments on real datasets from a variety of medical domains.