  High-level classification algorithms focus on the interactions between
instances. These produce a new form to evaluate and classify data. In this
process, the core is a complex network building methodology. The current
methodologies use variations of kNN to produce these graphs. However, these
techniques ignore some hidden patterns between attributes and require
normalization to be accurate. In this paper, we propose a new methodology for
network building based on attribute-attribute interactions that do not require
normalization. The current results show us that this approach improves the
accuracy of the high-level classification algorithm based on betweenness
centrality.
