A Genetics Clustering-based Approach for Weblog Data Cleaning

This paper addresses the issue of Weblog Data cleaning within the scope of Web Usage Mining. Weblog data are information on end-user clicks and underlying user-agent hits recorded by webservers. Since Web Usage Mining is interested in end-user clicks, user-agent hits are referred to as noise to be cleaned before mining. The most referenced and implemented cleaning methods are the conventional and advanced cleaning. They are content-centric filtering heuristics based on the requested resource attribute of weblog databases. These cleaning methods are limited in terms of relevancy, workability and cost constraints, within the context of dynamic and responsive web. In order to deal with these constraints, this contribution introduces a clustering-based cleaning method focused on the genetic features of the logging structure. The introduced cleaning method mines clicks from hits on the basis of their underlying genetics features and statistical properties. The genetics clustering-based cleaning experimentation demonstrates significant advantages compared to the content-centric methods.