M. Kuramochi and G. Karypis. Discovering frequent geometric subgraphs. In Proceedings of the 2002. Home/Search Document Details and Download
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Feature Mining Paradigms for Scientific Data - Jiang, Choy, Mehta, Wilkins (Correct)
....with applications ranging from fluid dynamics to chemical compounds. By modeling the features in a dataset with graphs, the problem of finding frequent patterns in the dataset becomes that of discovering subgraphs that occur frequently throughout the entire set of graphs. Kuramochi and Karypis [17, 18] developed an e#cient algorithm for discovering frequent subgraphs and applied it to chemical compound datasets. A similar technique can also be applied to two dimensional turbulent flow fields. Graphs can be constructed with the nodes representing the spatial location of coherent structures ....
M. Kuramochi and G. Karypis. Discovering Frequent Geometric Subgraphs. In IEEE Intl. Conference on Data Mining '02, December 2002.
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M. Kuramochi and G. Karypis. Discovering frequent geometric subgraphs. In Proceedings of the 2002.
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