Hierarhical Forest Custer and Heuristic Poll Classification For Mining Web Access Logs
Journal Title: International Journal of Engineering and Science Invention - Year 2019, Vol 8, Issue 1
Abstract
Modern web clickstream data comprises multi levels of granularity based on the visitor behavior on its websites, making it cumbersome to scrutinize. Following the overarching idea that the event visualization should provide information regarding the click events at multiple levels of granularity and identifying navigation patterns across several applications, three levels of granularity are identified for analyzing the navigation patterns, namely, web access sequences, set of all users along with log entry. This method is called as Hierarchical Forest Cluster and Heuristic Poll Classification (HFC-HPC). With the objective of mining web access logs and improving the scalability of visualization interface, three parts are designed. They are preprocessing using Localized Sequence Level model, clustering using Certainty Hierarchical Forest and classification via Heuristic Poll. Three identifications are considered during preprocessing namely, user, user session and page view identifications to extract information of interest with minimal time. Followed by the extracted information, Certainty Hierarchical Forest Clustering algorithm is applied with which optimal clusters are said to be generated with minimal computation overhead. Finally, heuristic poll is applied to the generated optimal clusters of applications to address scalability in the context of high level patterns. Experimental results based on web log files illustrate the applicability of the proposed method in achieving better performance than existing methods in terms of computational time, overhead and error.
Authors and Affiliations
B. K. Mathan Nagan, Dr. C. Chandrasekar
Keywords
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