Clustering Web Usage Data Using Concept Hierarchy and Self Organizing Map

Clustering Web Usage data is one of the important tasks of Web Usage Mining, which helps to find Web user clusters and Web page clusters. Web user clusters establish groups of users exhibiting similar browsing patterns and Web page clusters provide useful knowledge to personalized Web services. Different types of clustering algorithms such as partition based, distance based, density based, grid based, hierarchical and fuzzy clustering algorithms are used to find clusters from Web usage data. These clustering algorithms require more space and time for larger Web server log files. K-Means algorithm has been one of the most widely used algorithms for clustering Web usage data due to its computational performance.

Provided by: International Journal of Computer Applications Topic: Software Date Added: Oct 2012 Format: PDF

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