Graph-Based Knowledge Discovery: Compression versus Frequency
نویسندگان
چکیده
There are two primary types of graph-based data miners: frequent subgraph and compression-based miners. With frequent subgraph miners, the most interesting substructure is the largest one (or ones) that meet the minimum support. Whereas, compression-based graph miners discover those subgraphs that maximize the amount of compression that a particular substructure provides a graph. The algorithms associated with these two approaches are not only different, but they also may result in dramatic performance differences, as well as in the normative patterns being discovered. In order to compare these two types of graphbased approaches to knowledge discovery, in the following sections we will compare two publicly available applications: GASTON and SUBDUE.
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