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The Deep Coalescence Consensus Tree Problem is Pareto on Clusters.

, , and . ISBRA, volume 6674 of Lecture Notes in Computer Science, page 172-183. Springer, (2011)

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Triplet supertree heuristics for the tree of life., , and . BMC Bioinform., (2009)Consensus properties for the deep coalescence problem and their application for scalable tree search., , and . BMC Bioinform., 13 (S-10): S12 (2012)Learning classifiers from remote RDF data stores augmented with RDFS subclass hierarchies., , and . IEEE BigData, page 1807-1813. IEEE Computer Society, (2015)Learning Classifiers from Distributional Data., , , and . BigData Congress, page 302-309. IEEE Computer Society, (2013)Learning Relational Bayesian Classifiers from RDF Data., , and . ISWC (1), volume 7031 of Lecture Notes in Computer Science, page 389-404. Springer, (2011)The Deep Coalescence Consensus Tree Problem is Pareto on Clusters., , and . ISBRA, volume 6674 of Lecture Notes in Computer Science, page 172-183. Springer, (2011)Consensus properties and their large-scale applications for the gene duplication problem., , and . J. Bioinform. Comput. Biol., 14 (3): 1642005:1-1642005:17 (2016)Clustering remote RDF data using SPARQL update queries., , and . ICDE Workshops, page 236-242. IEEE Computer Society, (2013)Learning Classifiers from Chains of Multiple Interlinked RDF Data Stores., and . BigData Congress, page 94-101. IEEE Computer Society, (2013)