Artificial Immune System (AIS) models offer a promising approach to data analysis and pattern recognition. However, in order
to achieve a desired learning capability (for example detecting all clusters in a dat set), current models require the
storage and manipulation of a large network of B Cells (with a number often exceeding the number of data points in addition to all the pairwise links
between these B Cells). Hence, current AIS models are far from being scalable, which makes them of limited use, even for medium
size data sets.
We propose a new scalable AIS learning approach that exhibits superior learning abilities, while at the same time, requiring
modest memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared
to current approaches is expected to be its ease of adaptation in dynamic environments. We illustrate the ability of the proposed
approach in detecting clusters in noisy data.
Keywords Artificial immune systems - scalability - clustering - evolutionary computation - dynamic learning