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 techniqueswhich 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.