Knowledge reduction in formal contexts using non-negative matrix factorization

Aswani Kumar Ch., Sérgio M. Dias and Newton J. Vieira

Mathematics and Computers in Simulation (MATCOM), 2015, vol. 109, issue C, 46-63

Abstract: Formal Concept Analysis (FCA) is a mathematical framework that offers conceptual data analysis and knowledge discovery. One of the main issues of knowledge discovery is knowledge reduction. The objective of this paper is to investigate the knowledge reduction in FCA and propose a method based on Non-Negative Matrix Factorization (NMF) for addressing the issue. Experiments on real world and benchmark datasets offer the evidence for the performance of the proposed method.

Keywords: Concept lattice; Formal concept analysis; Knowledge reduction; Non-negative matrix factorization; Singular value decomposition (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475414001992
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:109:y:2015:i:c:p:46-63

DOI: 10.1016/j.matcom.2014.08.004

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().