The Role of Hypermutation and Affinity Maturation in AIS Approaches to Clustering

Abstract

In recent years, several artificial immune system (AIS) approaches have been proposed for unsupervised learning. Generally, in these approaches antibodies (or B-cells) are considered as clusters and antigens are data samples or instances. Moreover, antigens are trapped through free-floating antibodies or immunoglobulins. In all these approaches, hypermutation plays an important role. Hypermutation is responsible for producing mutated copies of stimulated antibodies/B-cells to capture similar antigens with higher affinity (similarity) measure and responsible to create diverse pool of solutions. Humoral-Mediated Artificial Immune System (HAIS) is an example of such algorithms. However, there is currently little understanding about the effectiveness of hypermutation operator in AIS approaches. In this chapter, we investigate the role of the hypermutation operator as well as affinity threshold (AT) parameters in order to achieve efficient clustering solutions. We propose a three-step methodology to examine the importance of hypermutation and the AT parameters in AIS approaches to clustering using basic concepts of HAIS algorithm. Here, the role of hypermutation in under-fitting and over-fitting the data will be discussed in the context of measure of entropy.