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Knowledge discovery in data using formal concept analysis and random projections

100%
Aswani Kumar C.
International Journal of Applied Mathematics and Computer Science
|
2011
|
tom 21
|
nr 4
745-756
EN
In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.
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A comprehensive survey on formal concept analysis, its research trends and applications

51%
Singh P., Aswani Kumar C., Gani A.
International Journal of Applied Mathematics and Computer Science
|
2016
|
tom 26
|
nr 2
495-516
EN
In recent years, FCA has received significant attention from research communities of various fields. Further, the theory of FCA is being extended into different frontiers and augmented with other knowledge representation frameworks. In this backdrop, this paper aims to provide an understanding of the necessary mathematical background for each extension of FCA like FCA with granular computing, a fuzzy setting, interval-valued, possibility theory, triadic, factor concepts and handling incomplete data. Subsequently, the paper illustrates emerging trends for each extension with applications. To this end, we summarize more than 350 recent (published after 2011) research papers indexed in Google Scholar, IEEE Xplore, ScienceDirect, Scopus, SpringerLink, and a few authoritative fundamental papers.
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