Computational Methods for Rough Classification and Discovery

@article{Bell1998ComputationalMF,
  title={Computational Methods for Rough Classification and Discovery},
  author={D. Bell and J. Guan},
  journal={J. Am. Soc. Inf. Sci.},
  year={1998},
  volume={49},
  pages={403-414}
}
  • 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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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 11 REFERENCES
    Rough Sets
    • 6,053
    • PDF
    A Decision Theoretic Framework for Approximating Concepts
    • 503
    • PDF
    From Data Properties to Evidence
    • 37
    Fuzzy Logic Foundations and Industrial Applications
    • 15
    • PDF
    Evidence Theory and Its Applications
    • 230
    Managing uncertainty in expert systems
    • 176
    • Highly Influential
    Designing a Kernel for Data Mining
    • 55
    Algebraic Aspects of Attribute Dependencies in Information Systems
    • 44
    The Discovery, Analysis, and Representation of Data Dependencies in Databases
    • 203
    • Highly Influential