TY - UNPB
T1 - Finding all maximal cliques in dynamic graphs
AU - Stix, Volker
PY - 2002
Y1 - 2002
N2 - Clustering applications dealing with perception based or biased data lead to models with non-disjunct clusters. There, objects to be clustered are allowed to belong to several clusters at the same time which results in a fuzzy clustering. It can be shown that this is equivalent to searching all maximal cliques in dynamic graphs like G_t=(V,E_t), where E_(t-1) in E_t, t=1,... ,T; E_0=(). In this article algorithms are provided to track all maximal cliques in a fully dynamic graph. It is naturally to raise the question about the maximum clique, having all maximal cliques. Therefore this article discusses potentials and drawbacks for this problem as well. (author's abstract)
AB - Clustering applications dealing with perception based or biased data lead to models with non-disjunct clusters. There, objects to be clustered are allowed to belong to several clusters at the same time which results in a fuzzy clustering. It can be shown that this is equivalent to searching all maximal cliques in dynamic graphs like G_t=(V,E_t), where E_(t-1) in E_t, t=1,... ,T; E_0=(). In this article algorithms are provided to track all maximal cliques in a fully dynamic graph. It is naturally to raise the question about the maximum clique, having all maximal cliques. Therefore this article discusses potentials and drawbacks for this problem as well. (author's abstract)
U2 - 10.57938/a0b23ef8-1972-4428-9f3a-74b513932cfe
DO - 10.57938/a0b23ef8-1972-4428-9f3a-74b513932cfe
M3 - WU Working Paper
T3 - Working Papers on Information Systems, Information Business and Operations
BT - Finding all maximal cliques in dynamic graphs
PB - Institut für Informationsverarbeitung und Informationswirtschaft, WU Vienna University of Economics and Business
CY - Vienna
ER -