Computational Methods for Rough Classification and Discovery

  title={Computational Methods for Rough Classification and Discovery},
  author={D. Bell and J. Guan},
  journal={J. Am. Soc. Inf. Sci.},
  • D. Bell, J. Guan
  • Published 1998
  • Computer Science
  • J. Am. Soc. Inf. Sci.
  • Rough set theory is a new mathematical tool to deal domain knowledge at the outset to distinguish some attriwith vagueness and uncertainty. To apply the theory, it butes as being the decision variables in decision trees is important to associate it with efficient and effective parlance. Frequently, these attributes can be identified computational methods. With a little adjustment, a relaeasily with an application domain. A decision attribute tion can be used to represent a decision table for… CONTINUE READING
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    Rough Sets
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    • 230
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    • 55
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    • 44
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