Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts

DOI

10.1080/18756891.2013.819179How to use a DOI?

Keywords

formal concept analysis (FCA), bipartite graph, small world, clustering, possibility theory

Abstract

The paper first offers a parallel between two approaches to conceptual clustering, namely formal concept analysis (augmented with the introduction of new operators) and bipartite graph analysis. It is shown that a formal concept (as defined in formal concept analysis) corresponds to the idea of a maximal bi-clique, while sub-contexts, which correspond to independent “conceptual worlds” that can be characterized by means of the new operators introduced, are disconnected sub-graphs in a bipartite graph. The parallel between formal concept analysis and bipartite graph analysis is further exploited by considering “approximation” methods on both sides. It leads to suggest new ideas for providing simplified views of datasets, taking also inspiration from the search for approximate itemsets in data mining (with relaxed requirements), and the detection of communities in hierarchical small worlds.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Bruno Gaume
AU  - Emmanuel Navarro
AU  - Henri Prade
PY  - 2013
DA  - 2013/11/01
TI  - Clustering bipartite graphs in terms of approximate formal concepts and sub-contexts
JO  - International Journal of Computational Intelligence Systems
SP  - 1125
EP  - 1142
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.819179
DO  - 10.1080/18756891.2013.819179
ID  - Gaume2013
ER  -