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Dynamic Clustering for Improved Web Usage Mining Using WebBLUEGILL

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Every year zettabytes of information are generated making difficult to find information on the web. Next e-business requirement will be personalizing or customizing the web pages according to the requirement of individuals. Web personalization provides recommendations that will be potentially liked by the user using web usage mining techniques. To support improved web personalization this paper proposes clustering algorithms handling two main challenges of web usage mining techniques: scalability and ability to handle dynamic data sets. Hybridization of the proposed WebBLUEGILL with the traditional k-means and the spherical k-means improves scalability. The WebBLUEGILL algorithm performs dynamic clustering with dynamic data items. The WebBLUEGILL algorithm uses swarm intelligence approach that gains inspiration by the Bluegill fish’s foraging behavior. This dynamic clustering approach can address the dynamic nature of online usage data by managing new data items and by handling budding and vanishing clusters.
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Cluster Optimization; Dynamic Clustering; Swarm Intelligence; User Profiles; Web Usage Mining

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