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		   B O S T O N   U N I V E R S I T Y
                                       
		      Computer Science Department
                                       
			  C O L L O Q U I U M
                                       
		       Wednesday October 25, 1995
                                       
				 3:00pm
		       (Coffee served at 2:45pm)
                                       
			 Seminar Room / MCS 135
                                       

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		  Data Mining and Knowledge Discovery
		       Gregory Piatetsky-Shapiro
			    GTE Laboratories

  One of the most promising areas in Data Mining and Knowledge Discovery
  is the automatic analysis of deviations.  Success in this task hinges
  on the ability to identify a few important and relevant events among
  the multitude of potentially interesting deviations.  In this talk we
  present our approach to determining the interestingness of a deviation
  via the potential benefit from a relevant action.  

  This approach has been implemented in the Key Findings Reporter (KEFIR), a
  system for discovering and explaining ``key findings'' in large,
  changing databases, currently being applied to the analysis of
  healthcare data. The system performs an automatic drill-down through
  data along multiple dimensions to determine the most interesting
  deviations of specific quantitative measures relative to their
  previous and expected values. It explains ``key'' deviations through
  their relationship to other deviations in the data, and, where
  appropriate, generates recommendations for actions in response to
  these deviations.  KEFIR uses Netscape, a WWW browser, to present its
  findings in a hypertext report, using natural language and business
  graphics.

Host: Prof. Mark Crovella
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For colloquium info, including directions, see http://cs-www.bu.edu/colloquium
   For more information contact Prof. Mark Crovella <crovella@bu.edu>
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