NACOD: A Naïve Associative Classifier for Online Data

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.