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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages226-235
Number of pages10
Volume6190 LNCS
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)03029743
ISSN (Electronic)16113349

Other

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

Fingerprint

Formal concept analysis
Formal Concept Analysis
Rough Set
Attribute
Approximation
Interval
Feature extraction
Information systems
Feature Extraction
Information Systems
Object

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yamaguchi, D., Murata, A., Li, G. D., & Nagai, M. (2010). Rough set approximations in formal concept analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6190 LNCS, pp. 226-235). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6190 LNCS). https://doi.org/10.1007/978-3-642-14467-7_12

Rough set approximations in formal concept analysis. / Yamaguchi, Daisuke; Murata, Atsuo; Li, Guo Dong; Nagai, Masatake.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6190 LNCS 2010. p. 226-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6190 LNCS).

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

Yamaguchi, D, Murata, A, Li, GD & Nagai, M 2010, Rough set approximations in formal concept analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6190 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6190 LNCS, pp. 226-235, Rough Set and Knowledge Technology Conference, RSKT 2008, Chengdu, China, 5/1/08. https://doi.org/10.1007/978-3-642-14467-7_12
Yamaguchi D, Murata A, Li GD, Nagai M. Rough set approximations in formal concept analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6190 LNCS. 2010. p. 226-235. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-14467-7_12
Yamaguchi, Daisuke ; Murata, Atsuo ; Li, Guo Dong ; Nagai, Masatake. / Rough set approximations in formal concept analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6190 LNCS 2010. pp. 226-235 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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