Conclusion

  • Robert J. Hilderman
  • Howard J. Hamilton
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 638)

Abstract

The objective of this book was to develop and evaluate a technique for ranking the interestingness of discovered patterns in data. Four goals were realized in obtaining this objective:
  • DGGs, a data structure for describing and guiding the generation of summaries from databases, were introduced.

  • Serial and parallel algorithms for traversing the generalization space described by DGGs were introduced and evaluated.

  • The use of diversity measures as measures of interestingness for summaries generated from databases was introduced and evaluated.

  • A preliminary foundation for a theory of interestingness within the context of ranking the interestingness of summaries generated from databases was developed.

Keywords

Diversity Measure Data Mining Technique Unknown Distribution Interestingness Measure Discovery Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Robert J. Hilderman
    • 1
  • Howard J. Hamilton
    • 1
  1. 1.University of ReginaCanada

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