Preprocessing and Unsupervised Approach For Web Usage Mining

Predicting of user’s browsing behavior is an important technology of E-commerce application. The prediction results can be used for personalization, building proper web site, improving marketing strategy, promotion, product supply, getting marketing information, forecasting market trends, and increasing the competitive strength of enterprises etc. Web Usage Mining is the application of data mining techniques to discover interesting usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining is usually an automated process whereby Web servers collect and report user access patterns in server access logs. The navigation datasets which are sequential in nature. Clustering web data is finding the groups which share common interests and behavior by analyzing the data collected in the web servers, this improves clustering on web data efficiently using proposed robust algorithm. In the proposed work a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model use multi-layered network architecture with a back propagation learning mechanism to discover and analyze useful knowledge from the available Web log data.