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/).
Cite this article
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 -