Skip Navigation U.S. Department of Health and Human Services www.hhs.gov
Agency for Healthcare Research Quality www.ahrq.gov
Archive print banner

Slide Presentation from the AHRQ 2007 Annual Conference

This information is for reference purposes only. It was current when produced and may now be outdated. Archive material is no longer maintained, and some links may not work. Persons with disabilities having difficulty accessing this information should contact us at: https://info.ahrq.gov. Let us know the nature of the problem, the Web address of what you want, and your contact information.

Please go to www.ahrq.gov for current information.


Clustering/CBR

Text Description is below the image.
  • Clustering divides large data sets into coherent subsets that can be studied more easily
  • Given an event report, CBR will
    • go through all event reports in database
    • compute similarity between them
    • find all reports within a certain distance or similarity (defined by the user)
  • These reports form a cluster

Notes:

Clustering Algorithms
There are many algorithms used to create clusters
Here we will discuss :
case-based reasoning

As an overgeneralization, all clustering algorithms basically do what was described in the previous slides: they divide the data into subsets based on some criterion of "distance." The two techniques presented here use different definitions of "distance." Statistical clustering uses numerical distance, while case-based reasoning uses distance between semantic concepts.


Previous Slide Previous Slide         Contents         Next Slide Next Slide


 

The information on this page is archived and provided for reference purposes only.

 

AHRQ Advancing Excellence in Health Care