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Rough Inclusion Functions and Similarity Indices

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EN
Abstrakty
EN
Rough inclusion functions are mappings considered in rough set theory with which one can measure the degree of inclusion of a set (information granule) in a set (information granule) in line with rough mereology. On the other hand, similarity indices are mappings used in cluster analysis with which one can compare clusterings, and clustering methods with respect to similarity. In this article we show that a large number of similarity indices, known from the literature, can be generated by three simple rough inclusion functions, the standard rough inclusion function included.
Wydawca
Rocznik
Strony
149--163
Opis fizyczny
Bibliogr. 49 poz.
Twórcy
  • Institute of Informatics Białystok University, Sosnowa 64, 15-887 Białystok, Poland
autor
  • Department of Logic and Methodology of Science, Maria Curie-Skłodowska University, pl. Maria Curie-Skłodowska 4, 20-031 Lublin, Poland
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-aa5db459-0bc0-4519-9e26-fbaed02f080f
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