Handling of Numeric Ranges with the Subdue System

Graph-based knowledge discovery has become a powerful tool in the machine learning and data mining areas. It provides a flexible and natural data representation to describe real world domains. In this research work we present a novel algorithm for graph-based approaches to deal with numerical attributes during the data processing phase implemented in the Subdue system. Our experimental results show that the use of numerical attributes increased classification accuracy in the Mutagenesis and PTC domains in 22% compared to the Subdue system when it does not use our numerical attributes handling approach. Our method also outperforms other author's results for the same domains, around 7% for the Mutagenesis domain and around 17% for the PTC domain.