  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 the complex network building methodology because it
determines the metrics to be used for classification. The current methodologies
use variations of kNN to produce these graphs. However, this technique ignores
some hidden pattern 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 and
capture the hidden patterns of the attributes. The current results show us that
could be used to improve some current high-level techniques.
