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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.
Date : 2025-12-23 Size : 89kb User : namareq
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