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Combining near-optimal feature selection with gSpan

Combining near-optimal feature selection with gSpan

This video was recorded at 6th International Workshop on Mining and Learning with Graphs (MLG), Helsinki 2008. Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerized scene analysis, and anomaly detection in program flows. Among the various approaches proposed in the literature, graph classification based on frequent subgraphs is a popular branch: Graphs are represented as (usually binary) vectors, with components indicating whether a graph contains a particular subgraph that is frequent across the dataset. On large graphs, however, one faces the enormous problem that the number of these frequent subgraphs may grow exponentially with the size of the graphs, but only few of them possess enough... Show More
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