A novel geometric graph miner and its applications

There are many problems where graphs are involved; some of them use geometric information. In the former context, the geometric subgraph mining algorithms have many applications, since resulting patterns provide relevant information for areas like biometrics, computer vision or molecular classification. Despite this fact, in the literature, there are only a few of geometric graph miners, and most of them have restrictions reducing the availability for application on real environments. In this paper, a new geometric subgraph mining algorithm is proposed. This algorithm, named GeoSuM, uses a pattern growth approach based on the already known non-geometric graph miner, gSpan. The proposal is very efficient in comparison with other state of the art approaches, in terms of execution time of the mining process. GeoSuM was tested in two different applications: molecular classification and fingerprint matching. Experimental results prove the flexibility and impact of GeoSuM in different scenarios.