Structural Graph-Based Representations Used for Finding Hidden Patterns

Abstract. In graph-based data mining (GBDM) tasks, an accurate data representation is fundamental for finding hidden patterns. However, there does not exist a standard representation to describe structural data because of the specific domain characteristics. Then, different graph topologies could be used as data representation, which is a challenge for GBDM tools. In this paper we explore a methodology for discovering hidden patterns in domains where is used a graph based representation. Our methodology is divided in three phases: first, we propose a formal graph notation used to symbolizes our graphs; second, we perform the data mining phase using the SI-COBRA and the SUBDUE tools; finally, we show how could be interpreted the outputs of these tools. We performed a set of experiments in two different domains where our methodology was applied: the web log and the SAT domains. With these examples we show how it is possible to symbolize our graphs with our notation, and also perform the GBDM task with the selected tools.