Clustering data in stationary environments with a local network neighborhood artificial immune system

The network theory in immunology inspired the modeling of network based artificial immune system (AIS) models for data clustering. Current network based AIS models determine the network connectivity between artificial lymphocytes (ALCs) by measuring the spatial distance between these ALCs against a distance threshold or by grouping ALCs into sub-networks. This paper discusses alternative network topologies to determine the network connectivity between ALCs and the advantages of using these network topologies. The local network neighborhood AIS model is then proposed as a network based AIS model which uses an index-based ALC neighborhood to determine the network connectivity between ALCs. The proposed model is compared to existing network based AIS models which are applied to data clustering problems. Furthermore, a sensitivity analysis is also done on the proposed model to investigate the influence of the model’s parameters on the quality of the clusters. The paper also gives a formal definition of data clustering and discusses the performance measures used to determine the quality of clusters.

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