Research Proposal on Distinct Study and Significant of Search Techniques in Web Mining

Abstract

The goal of this research is to provide a more current evaluation and update of web mining research and how machine learning techniques can be applied to web mining techniques available. Currenttrends in each of the three different types of web mining are reviewed in the categories of web content mining, web usage mining, and web structure mining.Unlike previous investigators, we divide web mining processes into the following five subtasks such as resource finding and retrieving, information selection and preprocessing, patterns analysis and recognition, validation and interpretation, and visualization.Major limitations of web mining research are lack of suitable test collections that can be reused by researchers and difficulty to collect web usage data across different web sites. Most web mining applications have been reviewed in this research. Although the activities are still in their early stages and should continue to develop as the Web evolves. This research shows that frequent pattern growth algorithm produces more efficient and accurate results to compare with K-Apriori algorithm. The proposed methods were successfully tested and results were observed and compared with existing methods on the web log files using machine learning techniques.