Advanced Web Usage Mining Algorithm using Neural Network and Principal Component Analysis

Web Usage Mining becomes a vital aspect in network traffic analysis. Previous study on Web usage mining using a synchronized Clustering, Neural based approach has shown that the usage trend analysis very much depends on the performance of the clustering of the number of requests. Self Organizing Networks is useful for representation of building unsupervised learning, clustering, and Visualization and feature maps. The preprocessed web log files are used for clustering. Growing Neural Gas is one of the types of Self Organizing Networks. The process details the transformation necessaries to adapt the data storage in the Web Servers Log files to an input of Growing Neural Gas algorithm so that we get the result without supervising the trained network. In this paper we are presenting a novel algorithm for clustering the web usage mining data to detect patterns. Self Organizing Map identifies the Winning neurons which are used in growing neural gas algorithm with Euclidean distance measure. Thus the proposed algorithm is hybrid and it combines Artificial Neural Network (ANN) and Principal Component Analysis (PCA). Keywords-Growing Neural Gas, Clustering, PCA (Principal Component Analysis), Artificial Neural Network (ANN), Web Log Clustering Algorithm, Self Organizing Map (SOM).