| Resumen |
Analyzing data in real time constitutes a challenge nowadays, due to the constant generation of
data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making
algorithm within the associative approach. The proposed algorithm, named Naïve Associative Classifier for
Online Data (NACOD), is able to deal with hybrid as well as with incomplete data. In addition, NACOD
is transparent and transportable, which makes it a very useful decision-maker in environments that require
such properties. The numerical experiments carried out show the effectiveness of NACOD. |
