Description: This paper explores basic aspects of the immune system and proposes a novel immune
network model with the main goals of clustering and filtering redundant data from
problems described by a set of discrete samples. It is not our concern to reproduce with
confidence any immune phenomenon, but to show that immune concepts can be used to
develop novel computational tools for data processing. As important results of our
model, the network evolved will be capable of reducing redundancy, describing data
structure, shapes and their cluster inter-relations. The data clustering approach will be
will be implemented in association with a statistical technique, and the network
performance will be illustrated using two benchmark problems. The paper is concluded
with a trade-off between the proposed network and artificial neural networks.
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