Concept lattices with negative information: {A} characterization theorem

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Authors

José Manuel Rodríguez-Jiménez

Pablo Cordero

Manuel Enciso

Sebastian Rudolph

Published

1 January 2016

Publication details

Inf. Sci. vol. 369 , pages 51–62.

Links

Abstract

Citation

Please, cite this work as:

[Rod+16] J. M. Rodr'-Jiménez, P. Cordero, M. Enciso, et al. “Concept lattices with negative information: A characterization theorem”. In: Inf. Sci. 369 (2016), pp. 51-62. DOI: 10.1016/J.INS.2016.06.015. URL: https://doi.org/10.1016/j.ins.2016.06.015.

BibTeX

@Article{RodriguezJimenez2016,
     author = {Jos{’e} Manuel Rodr'-Jim{’e}nez and Pablo Cordero and Manuel Enciso and Sebastian Rudolph},
     journal = {Inf. Sci.},
     title = {Concept lattices with negative information: {A} characterization theorem},
     year = {2016},
     pages = {51–62},
     volume = {369},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/journals/isci/Rodriguez-Jimenez16.bib},
     doi = {10.1016/J.INS.2016.06.015},
     timestamp = {Wed, 14 Nov 2018 00:00:00 +0100},
     url = {https://doi.org/10.1016/j.ins.2016.06.015},
}

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Citations
CrossRef - Citation Indexes: 13
Scopus - Citation Indexes: 37

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Mendeley - Readers: 8

Cites

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

[1] P. Cordero, M. Enciso, A. Mora, et al. “Inference of Mixed Information in Formal Concept Analysis”. In: Trends in Mathematics and Computational Intelligence. Springer International Publishing, Oct. 2018, p. 81–87. ISBN: 9783030004859. DOI: 10.1007/978-3-030-00485-9_9. URL: http://dx.doi.org/10.1007/978-3-030-00485-9_9.

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[3] D. Dubois, J. Medina, H. Prade, et al. “Disjunctive attribute dependencies in formal concept analysis under the epistemic view of formal contexts”. In: Information Sciences 561 (Jun. 2021), p. 31–51. ISSN: 0020-0255. DOI: 10.1016/j.ins.2020.12.085. URL: http://dx.doi.org/10.1016/j.ins.2020.12.085.

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[7] R. Janostik, J. Konecny, and P. Krajča. “Interface between Logical Analysis of Data and Formal Concept Analysis”. In: European Journal of Operational Research 284.2 (Jul. 2020), p. 792–800. ISSN: 0377-2217. DOI: 10.1016/j.ejor.2020.01.015. URL: http://dx.doi.org/10.1016/j.ejor.2020.01.015.

[8] J. Konecny. “Attribute implications in L-concept analysis with positive and negative attributes: Validity and properties of models”. In: International Journal of Approximate Reasoning 120 (May. 2020), p. 203–215. ISSN: 0888-613X. DOI: 10.1016/j.ijar.2020.02.009. URL: http://dx.doi.org/10.1016/j.ijar.2020.02.009.

[9] J. Konecny. “On attribute reduction in concept lattices: Methods based on discernibility matrix are outperformed by basic clarification and reduction”. In: Information Sciences 415–416 (Nov. 2017), p. 199–212. ISSN: 0020-0255. DOI: 10.1016/j.ins.2017.06.013. URL: http://dx.doi.org/10.1016/j.ins.2017.06.013.

[10] N. Leutwyler, M. Lezoche, C. Franciosi, et al. “Methods for concept analysis and multi-relational data mining: a systematic literature review”. In: Knowledge and Information Systems 66.9 (May. 2024), p. 5113–5150. ISSN: 0219-3116. DOI: 10.1007/s10115-024-02139-x. URL: http://dx.doi.org/10.1007/s10115-024-02139-x.

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[12] M. Ojeda‐Aciego and J. M. Rodriguez‐Jimenez. “Formal concept analysis with negative attributes for forgery detection”. In: Computational and Mathematical Methods 3.6 (Sep. 2020). ISSN: 2577-7408. DOI: 10.1002/cmm4.1124. URL: http://dx.doi.org/10.1002/cmm4.1124.

[13] F. Pérez-Gámez, P. Cordero, M. Enciso, et al. “Computing the Mixed Concept Lattice”. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2022, p. 87–99. ISBN: 9783031089718. DOI: 10.1007/978-3-031-08971-8_8. URL: http://dx.doi.org/10.1007/978-3-031-08971-8_8.

[14] F. Pérez-Gámez, P. Cordero, M. Enciso, et al. “A New Kind of Implication to Reason with Unknown Information”. In: Formal Concept Analysis. Springer International Publishing, 2021, p. 74–90. ISBN: 9783030778675. DOI: 10.1007/978-3-030-77867-5_5. URL: http://dx.doi.org/10.1007/978-3-030-77867-5_5.

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[16] F. Pérez-Gámez, D. López-Rodríguez, P. Cordero, et al. “Simplifying Implications with Positive and Negative Attributes: A Logic-Based Approach”. In: Mathematics 10.4 (Feb. 2022), p. 607. ISSN: 2227-7390. DOI: 10.3390/math10040607. URL: http://dx.doi.org/10.3390/math10040607.

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[19] B. Sinclair-Desgagne. “Mining for Unknown Unknowns”. In: SSRN Electronic Journal (2023). ISSN: 1556-5068. DOI: 10.2139/ssrn.4426652. URL: http://dx.doi.org/10.2139/ssrn.4426652.

[20] B. Sinclair-Desgagné. “Measuring innovation and innovativeness: a data-mining approach”. In: Quality & Quantity 56.4 (Sep. 2021), p. 2415–2434. ISSN: 1573-7845. DOI: 10.1007/s11135-021-01231-6. URL: http://dx.doi.org/10.1007/s11135-021-01231-6.

[21] B. Sinclair-Desgagné. “Mining for Unknown Unknowns”. In: Electronic Proceedings in Theoretical Computer Science 379 (Jul. 2023), p. 507–517. ISSN: 2075-2180. DOI: 10.4204/eptcs.379.38. URL: http://dx.doi.org/10.4204/eptcs.379.38.

[22] E. C. C. Tsang, B. Fan, D. Chen, et al. “Multi-level cognitive concept learning method oriented to data sets with fuzziness: a perspective from features”. In: Soft Computing 24.5 (Jun. 2019), p. 3753–3770. ISSN: 1433-7479. DOI: 10.1007/s00500-019-04144-7. URL: http://dx.doi.org/10.1007/s00500-019-04144-7.

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[24] H. Yu, Q. Li, and M. Cai. “Characteristics of three-way concept lattices and three-way rough concept lattices”. In: Knowledge-Based Systems 146 (Apr. 2018), p. 181–189. ISSN: 0950-7051. DOI: 10.1016/j.knosys.2018.02.007. URL: http://dx.doi.org/10.1016/j.knosys.2018.02.007.

[25] Y. Zhai, J. Qi, D. Li, et al. “The structure theorem of three-way concept lattice”. In: International Journal of Approximate Reasoning 146 (Jul. 2022), p. 157–173. ISSN: 0888-613X. DOI: 10.1016/j.ijar.2022.04.007. URL: http://dx.doi.org/10.1016/j.ijar.2022.04.007.