The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

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The mammalian immune system is a highly complex, inherently parallel, distributed system. The field of Artificial Immune Systems (AIS) has developed a wide variety of algorithms inspired by the immune system, few of which appear to capitalize on the parallel nature of the system from which inspiration was taken. The work in this paper presents the first steps at realizing a parallel artificial immune system for classification. A simple parallel version of the classification algorithm Artificial Immune Recognition System (AIRS) is presented. Initial results indicate that a decrease in overall runtime can be achieved through fairly naïve techniques. The need for more theoretical models of the behavior of the algorithm is discussed.

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With the recent introduction of third generation (3G) technology in the field of mobile commu-nications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With these usage patterns, the service provider can effectively target certain classes of customers who are more likely to purchase their subscription plans. To discover these patterns, we use a novel algorithm called Artificial Immune Recognition System (AIRS) that is based on the specificity of the human immune system. In our experiment, the algorithm performs well, achieving an accuracy rate in the range of 80% to 90%, depending on the set of parameter values used.