Knowledge reduction in formal contexts using non-negative matrix factorization

被引：68
作者：
Kumar, Ch. Aswani [1 ]
Dias, Sergio M. [2 ,3 ]
Vieira, Newton J. [2 ]
机构：
[1] VIT Univ, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India
[2] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG, Brazil
[3] Fed Serv Data Proc SERPRO, BR-31035536 Belo Horizonte, MG, Brazil
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2015年 / 109卷
关键词：
Concept lattice; Formal concept analysis; Knowledge reduction; Non-negative matrix factorization; Singular value decomposition; CONCEPT LATTICE REDUCTION; COMPLEXITY REDUCTION; RULE ACQUISITION; REPRESENTATION; JBOS;
D O I：
10.1016/j.matcom.2014.08.004
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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. (C) 2014 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：46 / 63
页数：18
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