Using sequential deviation to dynamically determine the number of clusters found by a local network neighbourhood artificial immune system

Many of the existing network theory based artificial immune systems have been applied to data clustering. The formation of artificial lymphocyte (ALC) networks represents potential clusters in the data. Although these models do not require any user specified parameter of the number of required clusters to cluster the data, these models do have a drawback in the techniques used to determine the number of ALC networks. This paper discusses the drawbacks of these techniques and proposes two alternative techniques which can be used with the local network neighbourhood artificial immune system. The end result is an enhanced model that can dynamically determine the number of clusters in a data set.