Rough set approximations in formal concept analysis

Daisuke Yamaguchi, Atsuo Murata, Guo Dong Li, Masatake Nagai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Conventional set approximations are based on a set of attributes; however, these approximations cannot relate an object to the corresponding attribute. In this study, a new model for set approximation based on individual attributes is proposed for interval-valued data. Defining an indiscernibility relation is omitted since each attribute value itself has a set of values. Two types of approximations, single- and multiattribute approximations, are presented. A multi-attribute approximation has two solutions: a maximum and a minimum solution. A maximum solution is a set of objects that satisfy the condition of approximation for at least one attribute. A minimum solution is a set of objects that satisfy the condition for all attributes. The proposed set approximation is helpful in finding the features of objects relating to condition attributes when interval-valued data are given. The proposed model contributes to feature extraction in interval-valued information systems.

Original languageEnglish
Title of host publicationTransactions on Rough Sets XII
Pages226-235
Number of pages10
DOIs
Publication statusPublished - 2010
EventRough Set and Knowledge Technology Conference, RSKT 2008 - Chengdu, China
Duration: May 1 2008May 1 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6190 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherRough Set and Knowledge Technology Conference, RSKT 2008
Country/TerritoryChina
CityChengdu
Period5/1/085/1/08

Keywords

  • grey numbers
  • grey system theory
  • indeterministic information systems
  • interval data
  • rough sets
  • set approximations

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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