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SIAM Journal on Computing
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SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Clustering Algorithms
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ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
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ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
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SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
All maximal independent sets and dynamic dominance for sparse graphs
ACM Transactions on Algorithms (TALG)
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ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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Proceedings of the 13th International Conference on Extending Database Technology
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Theoretical Computer Science
Finding maximal cliques in massive networks by H*-graph
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Finding maximal cliques in massive networks
ACM Transactions on Database Systems (TODS)
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Proceedings of the VLDB Endowment
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Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
All roads lead to Rome---New search methods for the optimal triangulation problem
International Journal of Approximate Reasoning
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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 Gt = (V,Et), where Et − 1 ⊂ Et, t = 1,…,T; E0 = &phis;. In this article algorithms are provided to track all maximal cliques in a fully dynamic graph.