Progressive Machine Learning Approach with WebAstro for Web Usage Mining
Harish Kumar, Anuradha, Anil Kumar Solanki, Krishna Kant Singh · Procedia Computer Science · 2020
Information retrieval is a scorching today when the flare-up of data on the Internet increasingly day by day. Due to the infinite growth and availability of the Internet; numbers of educational sites are amplified. Huge Expansion in knowledge puts a special effect on the education system. This helps in upgrading higher education. Interest in Web usage mining has grown swiftly in its short history, both in exploring interest and research. Web usage mining is a type of data mining that extracts the server log information after users browse web pages. For extracting and retrieval information machine learning approaches are designed to recognize useful information from text documents automatically. The main aim of this paper is to cram weblogs using the clustering technique with WEBASTRO tool for predicting next user movement and user behaviour analysis. This paper presents an ideal approach for web mining and predicting their behaviour for the next visit and automatic web site modification.