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Attribute reduction of SE-ISI concept lattices for incomplete contexts
Three-way concept analysis in incomplete contexts lays the theory dealing with the data in incomplete contexts, especially three kinds of partially known formal concepts including SE-ISI formal concept, ISE-SI formal concept and ISE-ISI formal concept. Generally speaking, not every attribute is esse...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474801/ https://www.ncbi.nlm.nih.gov/pubmed/32922163 http://dx.doi.org/10.1007/s00500-020-05271-2 |
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author | Wang, Zhen Wei, Ling Qi, Jianjun Qian, Ting |
author_facet | Wang, Zhen Wei, Ling Qi, Jianjun Qian, Ting |
author_sort | Wang, Zhen |
collection | PubMed |
description | Three-way concept analysis in incomplete contexts lays the theory dealing with the data in incomplete contexts, especially three kinds of partially known formal concepts including SE-ISI formal concept, ISE-SI formal concept and ISE-ISI formal concept. Generally speaking, not every attribute is essential in an incomplete context since the purpose of research is different. Thus, we propose four kinds of attribute reduction of SE-ISI concept lattices based on different criteria. Then, we discuss the relationships among the four kinds of attribute reduction, including the relationships among the consistent sets and relationships among the reducts. Finally, based on discernibility matrices and discernibility functions, the approaches to obtaining these attribute reduction are presented. |
format | Online Article Text |
id | pubmed-7474801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-74748012020-09-08 Attribute reduction of SE-ISI concept lattices for incomplete contexts Wang, Zhen Wei, Ling Qi, Jianjun Qian, Ting Soft comput Foundations Three-way concept analysis in incomplete contexts lays the theory dealing with the data in incomplete contexts, especially three kinds of partially known formal concepts including SE-ISI formal concept, ISE-SI formal concept and ISE-ISI formal concept. Generally speaking, not every attribute is essential in an incomplete context since the purpose of research is different. Thus, we propose four kinds of attribute reduction of SE-ISI concept lattices based on different criteria. Then, we discuss the relationships among the four kinds of attribute reduction, including the relationships among the consistent sets and relationships among the reducts. Finally, based on discernibility matrices and discernibility functions, the approaches to obtaining these attribute reduction are presented. Springer Berlin Heidelberg 2020-09-06 2020 /pmc/articles/PMC7474801/ /pubmed/32922163 http://dx.doi.org/10.1007/s00500-020-05271-2 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Foundations Wang, Zhen Wei, Ling Qi, Jianjun Qian, Ting Attribute reduction of SE-ISI concept lattices for incomplete contexts |
title | Attribute reduction of SE-ISI concept lattices for incomplete contexts |
title_full | Attribute reduction of SE-ISI concept lattices for incomplete contexts |
title_fullStr | Attribute reduction of SE-ISI concept lattices for incomplete contexts |
title_full_unstemmed | Attribute reduction of SE-ISI concept lattices for incomplete contexts |
title_short | Attribute reduction of SE-ISI concept lattices for incomplete contexts |
title_sort | attribute reduction of se-isi concept lattices for incomplete contexts |
topic | Foundations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7474801/ https://www.ncbi.nlm.nih.gov/pubmed/32922163 http://dx.doi.org/10.1007/s00500-020-05271-2 |
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