On the Development of an Optimized Web Usage Mining Tool

Web usage mining – a category of web mining and described as the process of extracting information from weblogs to extract patterns to make out some meaningful information therefrom. This paper reports on a new Optimized Web Usage Mining Tool (OWUMT). The web usage mining tool developed as a part of this research endeavour has incorporated newer classification algorithms namely Random Forest with Ant Colony Optimization, Random Forest with Genetic Algorithm, and Random Forest with Ant Colony Optimization and Genetic Algorithm have been incorporated in addition to traditional algorithms like Naïve Bayes and Decision Tree. The performance of three classification algorithms namely Random Forest, Naïve Bayes, and Decision Tree has been compared by executing these algorithms in OWUMT, Weka and Rapidminer, the latter two being available in the shareware domain. Selected algorithms have been used to classify the data contained in server log files. Experimental results of three classification algorithms have been evaluated on two parameters, namely, classification accuracy and error