<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Knowledge discovery in data using formal concept analysis and random projections

Networks and Information Security Division, School of Information Technology and Engineering, VIT University, Vellore, India

International Journal of Applied Mathematics and Computer Science

{"article-title":"Knowledge discovery in data using formal concept analysis and random projections"}

In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation...

Knowledge discovery in data using formal concept analysis and random projections

Online veröffentlicht: 21. Dez. 2011

Seitenbereich: 745 - 756

DOI: https://doi.org/10.2478/v10006-011-0059-1

Schlüsselwörter
attribute implications, concept lattices, dimensionality reduction, formal concept analysis, knowledge discovery, random projections

This content is open access.

Knowledge discovery in data using formal concept analysis and random projections

Online veröffentlicht: 21. Dez. 2011

Seitenbereich: 745 - 756

DOI: https://doi.org/10.2478/v10006-011-0059-1

Schlüsselwörterattribute implications, concept lattices, dimensionality reduction, formal concept analysis, knowledge discovery, random projections

This content is open access.

Schlüsselwörter
attribute implications, concept lattices, dimensionality reduction, formal concept analysis, knowledge discovery, random projections