ASPG Menu
search

American Scientific Publishing Group

verified Journal

Journal of Neutrosophic and Fuzzy Systems

ISSN
Online: 2771-6449 Print: 2771-6430
Frequency

Continuous publication

Publication Model

Open access journal. All articles are freely available online with no APC.

Journal of Neutrosophic and Fuzzy Systems
Full Length Article

Volume 2Issue 1PP: 31-39 • 2022

Data with Rough Attributes and Its Reduct Analysis

Prem Kumar Singh 1*
1Department of Computer Science and Engineering, Gandhi Institute of Technology and Management-Visakhapatnam, Andhra Pradesh 530045, India
* Corresponding Author.
Received July 26, 2021 Accepted: Jan 08, 2022

Abstract

Recent time many researchers focused on dealing the uncertainty and its characterization. The precise approximation of uncertainty in many-valued data set is one of the major tasks. It becomes more difficult in case the given data sets are non-Euclidean. Hence the rough fuzzy set and its graphical visualization is introduced in this paper for knowledge processing tasks.

Keywords

Fuzzy Rough graph Knowledge representation Many-valued attributes Non-Euclidean geometry Rough Set Rough graph

References

[1] Singh P. K., “Three-way fuzzy concept lattice representation using neutrosophic set”, International Journal of Machine Learning and Cybernetics,  Vol 8, Issue 1, pp. 69-79, 2017.

 [2] Singh PK, Ch. Aswani Kumar, “Concept lattice reduction using different subset of attributes as information granules”, Granular Computing, Vol. 2, Issue 3), pp. 159–173, 2017 

 [3] Singh PK, “AntiGeometry and NeutroGeometry characterization of Non-Euclidean data sets”, Journal of Neutrosophic and Fuzzy Systems, Vol 1, Issue 1, pp. 24-33, DOI: https://doi.org/10.54216/JNFS.0101012

[4] Singh PK, “Data with Non-Euclidean Geometry and its Characterization,” Journal of Artificial Intelligence and Technology, 2021, Vol. 2, Issue 1, pp-3-8., DOI: 10.37965/jait.2021.12001 

[5] Singh PK, “Cubic graph representation of concept lattice and its decomposition”, Evolving System, doi: 10.1007/s12530-021-09400-6 

[6] Pawlak Z, “Rough sets”, Int. J. Comput. Inf. Sci. Vol., pp. 341–356, 1982

[7] Pawlak Z, “Rough set theory and its applications to data analysis,” Cybern Syst Vol. 29, Issue 7, pp. 661–688, 1998 

[8] He T, Chan Y, Shi K, “Weighted rough graph and its application,” In: Proceedings of IEEE Sixth Int Conf Intell Syst Des Appl 1:486–492, 2006

[9] He T, “Rough properties of rough graph,” Appl Mech Mater Vol 157–158, pp. 517–520, 2012

[10] He T, “Representation form of rough graph,” Appl Mech Mater, Vol. 157–158, pp. 874–877, 2012 

[11] Liang M, Liang B, Wei L, Xu X, “Edge rough graph and its application,” In: Proc. Of eighth International Conference on Fuzzy Systems and Knowledge Discovery 2011, pp. 335–338 

[12] Wang S, Zhu Q, Zhu W, Min F, “Graph and matrix approaches to rough sets through matroids. Information Sciences, Vol. 288, pp. 1–11, 2014 

[13] Li W, Huang Z, Jia X, Cai X, “Neighborhood based decision-theoretic rough set models,” International Journal of Approximate Reasoning, Vol. 69, pp. 1–17, 2016

[14] Noor R, Irshad I, Javaid I, “Soft rough graphs”. arXiv preprint arXiv:1707.05837, 2017

[15] Fariha Z, Akram M, “A novel decision–making method based on rough fuzzy information,” Int J Fuzzy Syst Vol. 20, Issue 3, pp. 1000–1014, 2018

[16] Rehman N, Shah N, Ali MI, Park C, “Uncertainty measurement for neighborhood based soft covering rough graphs with applications,” RACSAM, Vol. 113, pp. 2515–2535, 2019

[17] Mathew B, John SJ, Garg H, “Vertex rough graphs,” Complex Intell. Syst. Vol 6, pp. 347– 353, 2020 

[18] Yao YY, “Relational interpretations of neighborhood operators and rough set approximation operators,” Inf. Sci., Vol. 101, pp. 239–259, 1998 

[19] Yao YY, “Three-way decisions with probabilistic rough sets,” Inf. Sci., Vol. 180, pp. 341–353, 2010

Cite This Article

Choose your preferred format

format_quote
Singh, Prem Kumar. "Data with Rough Attributes and Its Reduct Analysis." Journal of Neutrosophic and Fuzzy Systems, vol. Volume 2, no. Issue 1, 2022, pp. 31-39. DOI: https://doi.org/10.54216/JNFS.020104
Singh, P. (2022). Data with Rough Attributes and Its Reduct Analysis. Journal of Neutrosophic and Fuzzy Systems, Volume 2(Issue 1), 31-39. DOI: https://doi.org/10.54216/JNFS.020104
Singh, Prem Kumar. "Data with Rough Attributes and Its Reduct Analysis." Journal of Neutrosophic and Fuzzy Systems Volume 2, no. Issue 1 (2022): 31-39. DOI: https://doi.org/10.54216/JNFS.020104
Singh, P. (2022) 'Data with Rough Attributes and Its Reduct Analysis', Journal of Neutrosophic and Fuzzy Systems, Volume 2(Issue 1), pp. 31-39. DOI: https://doi.org/10.54216/JNFS.020104
Singh P. Data with Rough Attributes and Its Reduct Analysis. Journal of Neutrosophic and Fuzzy Systems. 2022;Volume 2(Issue 1):31-39. DOI: https://doi.org/10.54216/JNFS.020104
P. Singh, "Data with Rough Attributes and Its Reduct Analysis," Journal of Neutrosophic and Fuzzy Systems, vol. Volume 2, no. Issue 1, pp. 31-39, 2022. DOI: https://doi.org/10.54216/JNFS.020104
Digital Archive Ready