Research Article
Linking Data According to Their Degree of Representativeness (DoR)
@ARTICLE{10.4108/inis.2.4.e2, author={Fr\^{e}d\^{e}ric Blanchard and Amine A\~{n}t-Younes and Michel Herbin}, title={Linking Data According to Their Degree of Representativeness (DoR)}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={2}, number={4}, publisher={ICST}, journal_a={INIS}, year={2015}, month={6}, keywords={Mining complex data, Representativeness, Graph based data analysis}, doi={10.4108/inis.2.4.e2} }- Frédéric Blanchard
Amine Aït-Younes
Michel Herbin
Year: 2015
Linking Data According to Their Degree of Representativeness (DoR)
INIS
ICST
DOI: 10.4108/inis.2.4.e2
Abstract
This contribution addresses the problem of extracting some representative data from complex datasets and connecting them in a directed graph. First we define a degree of representativeness (DoR) inspired of the Borda voting procedure. Secondly we present a method to connect pairwise data using neighborhoods and the DoR as an objective function. We then present case studies as illustrative purposes: unsupervised grouping of binary images, analysis of co-authorships in a research team and structuration of a medical patient-oriented database.
Copyright © 2015 Frédéric Blanchard et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
