Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Case Study: Visualizing Sets of Evolutionary Trees
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
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In this paper we propose an innovative method of representing common knowledge in leaf-labelled trees as a set of frequent subsplits, together with its interpretation. Our technique is suitable for trees built on the same leafset as well as for trees where the leafset varies. The proposed solution has a very good interpretation, as it returns different, maximal sets of taxa that are connected with the same relations in the input trees. In contrast to other methods known in literature it does not necessarily result in one tree, but may result in a profile of trees, which are usually more resolved than the consensus trees.