Applying Sequence Alignment in Tracking Evolving Clusters of Web-Sessions Data: An Artificial Immune Network Approach

Artificial Immune System (AIS) models have outstanding properties, such as learning, adaptivity and robustness, which make them suitable for learning in dynamic and noisy environments such as the web. In this study, we tend to apply AIS for tracking evolving patterns of web usage data. The definition of the similarity of web sessions has an important impact on the quality of discovered patterns. Many prevalent web usage mining approaches ignore the sequential nature of web navigations for defining similarity between sessions. We propose the use of a new web sessions´ similarity measure for investigating the usage data from web access log files. In this similarity measure, in addition to the sequential nature of web navigations, the usage similarity of web sessions is taken into consideration. The ability of the AIS system to track evolving patterns of web usage is validated by applying the proposed method on real world web data.