Benefits of InterSite Pre-Processing and Clustering Methods in E-Commerce Domain

TL;DR

This paper demonstrates how advanced preprocessing and intersite-aware clustering methods improve analysis of e-commerce clickstream data, revealing valuable insights into user behavior across multiple sites.

Contribution

It introduces a comprehensive data warehouse built with advanced preprocessing considering intersite aspects and proposes a crossed-clustering method for enhanced analysis.

Findings

Intersite user visits are significant for clickstream analysis.

Preliminary results show benefits of the proposed clustering methods.

The approach offers promising insights, though further research is needed.

Abstract

This paper presents our preprocessing and clustering analysis on the clickstream dataset proposed for the ECMLPKDD 2005 Discovery Challenge. The main contributions of this article are double. First, after presenting the clickstream dataset, we show how we build a rich data warehouse based an advanced preprocesing. We take into account the intersite aspects in the given ecommerce domain, which offers an interesting data structuration. A preliminary statistical analysis based on time period clickstreams is given, emphasing the importance of intersite user visits in such a context. Secondly, we describe our crossed-clustering method which is applied on data generated from our data warehouse. Our preliminary results are interesting and promising illustrating the benefits of our WUM methods, even if more investigations are needed on the same dataset.