doi.org/10.32614/CRAN.package.RoughSets

Preview meta tags from the doi.org website.

Linked Hostnames

3

Thumbnail

Search Engine Appearance

Google

https://doi.org/10.32614/CRAN.package.RoughSets

RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.



Bing

RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

https://doi.org/10.32614/CRAN.package.RoughSets

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.



DuckDuckGo

https://doi.org/10.32614/CRAN.package.RoughSets

RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.

  • General Meta Tags

    10
    • title
      CRAN: Package RoughSets
    • Content-Type
      text/html; charset=utf-8
    • viewport
      width=device-width, initial-scale=1.0, user-scalable=yes
    • citation_title
      Data Analysis Using Rough Set and Fuzzy Rough Set Theories [R package RoughSets version 1.3-8]
    • citation_author1
      Andrzej Janusz

  • Open Graph Meta Tags

    5
    • og:title
      RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories
    • og:description
      Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
    • og:image
      https://CRAN.R-project.org/CRANlogo.png
    • og:type
      website
    • og:url
      https://CRAN.R-project.org/package=RoughSets

  • Twitter Meta Tags

    1
    • twitter:card
      summary

  • Link Tags

    2
    • canonical
      https://CRAN.R-project.org/package=RoughSets
    • stylesheet
      ../../CRAN_web.css

Links

4