Clustering Dynamic Web Usage Data

TL;DR

This paper presents an evolutionary clustering approach to analyze web usage data over time, effectively detecting concept drift and changes in user behavior by updating models with temporal summaries.

Contribution

It introduces a novel evolutionary clustering method that accounts for temporal dynamics in web usage data, addressing the challenge of concept drift in web analytics.

Findings

Effective detection of concept drift in web data

Improved model updating through temporal clustering

Validation with external evaluation criteria

Abstract

Most classification methods are based on the assumption that data conforms to a stationary distribution. The machine learning domain currently suffers from a lack of classification techniques that are able to detect the occurrence of a change in the underlying data distribution. Ignoring possible changes in the underlying concept, also known as concept drift, may degrade the performance of the classification model. Often these changes make the model inconsistent and regular updatings become necessary. Taking the temporal dimension into account during the analysis of Web usage data is a necessity, since the way a site is visited may indeed evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. One solution to this problem, proposed in this article, is to update models using summaries obtained by means of an evolutionary approach based on an intelligent clustering approach. We carry out various clustering strategies that are applied on time sub-periods. To validate our approach we apply two external evaluation criteria which compare different partitions from the same data set. Our experiments show that the proposed approach is efficient to detect the occurrence of changes.