Handling of Numeric Ranges for Graph-Based Knowledge Discovery

Nowadays, graph-based knowledge discovery algorithms do not consider numeric attributes (they are discarded in the preprocessing step, or they are treated as alphanumeric values with an exact matching criterion), with the limitation to work with domains that do not have this type of attribute or finding patterns without numeric attributes. In this work, we propose a new approach for the numerical attributes handling for graphbased learning algorithms. Our approach shows how graph-based learning approaches increase their accuracy for the classification task and its descriptive power when they are able to use both nominal and numerical attributes. This new approach was tested with the Subdue system in the mutagenesis and PTC domains showing an accuracy increase around 16% compared to Subdue when it does not use our numerical attributes handling algorithm. In some research areas such as data mining and machine learning, the domain data representation is a fundamental aspect that determin...

• Type: Conference
• Year: 2010
• Where: FLAIRS
• Authors: Oscar E. Romero, Jesus A. Gonzalez, Lawrence B. Holder
• Keywords: Artificial Intelligence, FLAIRS 2010, Knowledge Discovery Algorithm, Numeric Attributes, Numerical Attributes