The fuzzy artificial immune system: motivations, basic concepts, and application to clustering and Web profiling

The human immune system can be seen as a complex network structure that is able to respond to an almost unlimited multitude of foreign invaders such as viruses and bacteria. Hence, this parallel and distributed adaptive system promises tremendous potential in many intelligent computing applications, including Web mining. Some of these immunity-based techniques involve the development and analysis of algorithms that can identify patterns in observed data in order to make predictions about unseen data. In this paper, we introduce several new enhancements to deal with some of the weaknesses of previous artificial immune system models. In particular, we address the uncertainty and fuzziness inherent in the matching process that takes place between antibodies and antigens. This problem is handled by introducing a fuzzy artificial immune system. A fuzzy artificial immune system mimicking the body's adaptive learning and defense mechanism in the face of invading biological agents is used as a monitoring and learning system for a Web site in the face of all incoming Web requests.

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