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Soft Computing

, Volume 23, Issue 12, pp 4503–4520 | Cite as

Bipolar fuzzy concept learning using next neighbor and Euclidean distance

  • Prem Kumar SinghEmail author
Methodologies and Application

Abstract

To handle the bipolarity in data with fuzzy attributes the properties of bipolar fuzzy set are introduced in the concept lattice theory for precise representation of formal fuzzy concepts and their hierarchical order visualization. In this process, adequate understanding of meaningful pattern existing in bipolar fuzzy concept lattice becomes complex when its size becomes exponential. To resolve this problem, the current paper proposes two methods based on the properties of next neighbors and Euclidean distance with an illustrative example. It is also shown that the proposed method provides similar knowledge extraction when compared to available subset-based method drawing for bipolar fuzzy concept lattice.

Keywords

Bipolar fuzzy concept Bipolar information Formal concept analysis Formal fuzzy concept Fuzzy concept lattice Granular computing 

Notes

Acknowledgements

Author thanks the anonymous reviewers and editor for their insight to improve the presentation and quality of this paper.

Compliance with ethical standards

Conflict of interest

Author declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human or animal participants.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Amity Institute of Information TechnologyAmity UniversityNoidaIndia

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