The field of Artificial Immune Systems (AIS) is the use of the natural immune system as a metaphor for solving computational problems. A novel unsupervised machine-learning algorithm, inspired by the immune system, has been developed called AINE. Using various immunological metaphors, AINE evolves a network of objects, known as an Artificial Immune Network (AIN) that is a diverse representation of the data set being learnt. The results of AINE are visualised in a specially developed tool (aiVIS), which allows the user to interact with the network to perform exploratory analysis. aiVIS presents AINs in such as way as to build up an understanding of the make up of the data set, learning about subtle patterns and clusters within the data set and links between clusters. Unclassified items can then be introduced into the network so to further enhance the exploratory nature of the AIN. This paper provides an overview of the learning algorithm, but concentrates on the visualisation aspect of the work. The usefulness of using AIN for exploratory visualisation is investigated and an explanation of how aiVIS operates is presented
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