Fraud Detection in Online Content Mining Relies on the Random Forest Algorithm

Abstract

Web data mining extracts insights from the massive volume of Web data. This intelligence may improve search engine results, analyze consumer patterns, and detect fraud. Web content, structure, and use mining are the primary categories of web data mining. Web content mining analyzes text and multimedia on websites. online structure mining examines connections between online sites to determine web topology. Web use mining examines user clickstreams to understand browsing activity. Many methods, tools, and algorithms may be utilized for web data mining. Popular methods include keyword extraction, clustering, classification, and association rule mining. Web data mining technologies like Weka, RapidMiner, and KNIME are popular. Popular algorithms for web data mining include K-means, Naïve Bayes, and Apriori.