A New Clustering Approach based on Page's Path Similarity for Navigation Patterns Mining

TL;DR

This paper introduces a novel clustering method for web navigation patterns that uses page path similarity to improve the accuracy of identifying related web pages based on user navigation logs.

Contribution

The study presents a new clustering approach leveraging logical path similarity, enhancing the precision of web page grouping compared to existing methods.

Findings

The proposed method outperforms others in clustering accuracy.

Simulation results demonstrate higher precision in identifying related pages.

The approach effectively captures conceptual relations between web pages.

Abstract

In recent years, predicting the user's next request in web navigation has received much attention. An information source to be used for dealing with such problem is the left information by the previous web users stored at the web access log on the web servers. Purposed systems for this problem work based on this idea that if a large number of web users request specific pages of a website on a given session, it can be concluded that these pages are satisfying similar information needs, and therefore they are conceptually related. In this study, a new clustering approach is introduced that employs logical path storing of a website pages as another parameter which is regarded as a similarity parameter and conceptual relation between web pages. The results of simulation have shown that the proposed approach is more than others precise in determining the clusters.