An evolutionary immune network for data clustering

TLDR

Immune concepts are used to develop powerful computational tools for data processing. The paper proposes a novel immune network model aimed at clustering and filtering unlabeled numerical data sets. The network is implemented with a statistical inference technique and evaluated on two benchmark problems. The evolved network reduces redundancy, describes data structure and cluster shapes, and offers a trade‑off compared to artificial neural networks for unsupervised learning.

Abstract

This paper explores basic aspects of the immune system and proposes a novel immune network model with the main goals of clustering and filtering unlabelled numerical data sets. It is not our concern to reproduce with confidence any immune phenomenon, but to show that immune concepts can be used to develop powerful computational tools for data processing. As important results of our model, the network evolved will be capable of reducing redundancy, describing data structure, including the shape of the clusters. The network will be implemented in association with a statistical inference technique, and its performance will be illustrated using two benchmark problems. The paper is concluded with a trade-off between the proposed network and artificial neural networks used to perform unsupervised learning.