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Data Mining and Knowledge Discovery Handbook pp 445–468Cite as

Granular Computing and Rough Sets - An Incremental Development

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Summary

This chapter gives an overview and refinement of recent works on binary granular computing. For comparison and contrasting, granulation and partition are examined in parallel from the prospect of rough Set theory (RST).The key strength of RST is its capability in representing and processing knowledge in table formats. Even though such capabilities, for general granulation, are not available, this chapter illustrates and refines some such capability for binary granulation. In rough set theory, quotient sets, table representations, and concept hierarchy trees are all set theoretical, while in binary granulation, they are special kind of pretopological spaces, which is equivalent to a binary relation Here a pretopological space means a space that is equipped with a neighborhood system (NS). A NS is similar to the classical NS of a topological space, but without any axioms attached to it3.

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Authors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, CA, 95192, USA

    Tsau Young (’T. Y.’) Lin

  2. Institute of Information Science Academia Sinica, Taipei, 115, Taiwan

    Churn-Jung Liau

Authors
  1. Tsau Young (’T. Y.’) Lin
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  2. Churn-Jung Liau
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Correspondence to Tsau Young (’T. Y.’) Lin .

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Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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(’T. Y.’) Lin, T.Y., Liau, CJ. (2009). Granular Computing and Rough Sets - An Incremental Development. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_22

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_22

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