Frequent subgraph mining has always been an important issue in data mining. Several frequent graph mining methods have been developed for mining graph transactions. However, these methods become less usable when the dataset is a single large graph. Also, when the graph is too large to fit in main memory, alternative techniques are necessary to efficiently find frequent subgraphs. We investigate the task of frequent subgraph mining on a single large graph using sampling approaches and find that sampling is a feasible approach for this task. We evaluate different sampling methods and provide a novel sampling method called 'random areas selection sampling', which produces better results than all the current graph sampling approaches with customized parameters.
REF first sample from a single large graph and then mine the sample for frequent subgraphs.
Frequent subgraph mining on a single large graph using sampling techniques
MLG '10
Computer Science