Discovering parallel episodes in event sequences: Enhanced analysis of web page navigation patterns

Abstract

In the last decades and the growth of digital devices, the analysis of user navigation patterns on websites presents a significant challenge for organizations aiming to enhance their digital strategies. This thesis focuses on the field of episode mining and introduces our algorithm, developed by modifying one of the episode miming algorithms approach to efficiently handle parallel episodes. By analyzing sequences of web page visits,the proposed version enables the extraction of episode rules. with improved performance and scalability. Experimental results on real world datasets demonstrate that our algorithm offers notable gains in execution time and accuracy compared to traditional methods, making it a valuable tool for predicting user behavior and supporting data driven decision-making in web analytics.