Visual cluster exploration of web clickstream data

Jishang Wei, Zeqian Shen, Neel Sundaresan, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

66 Scopus citations


Web clickstream data are routinely collected to study how users browse the web or use a service. It is clear that the ability to recognize and summarize user behavior patterns from such data is valuable to e-commerce companies. In this paper, we introduce a visual analytics system to explore the various user behavior patterns reflected by distinct clickstream clusters. In a practical analysis scenario, the system first presents an overview of clickstream clusters using a Self-Organizing Map with Markov chain models. Then the analyst can interactively explore the clusters through an intuitive user interface. He can either obtain summarization of a selected group of data or further refine the clustering result. We evaluated our system using two different datasets from eBay. Analysts who were working on the same data have confirmed the system's effectiveness in extracting user behavior patterns from complex datasets and enhancing their ability to reason.

Original languageEnglish (US)
Title of host publicationIEEE Conference on Visual Analytics Science and Technology 2012, VAST 2012 - Proceedings
Number of pages10
StatePublished - Dec 1 2012
Event2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012 - Seattle, WA, United States
Duration: Oct 14 2012Oct 19 2012


Other2012 IEEE Conference on Visual Analytics Science and Technology, VAST 2012
Country/TerritoryUnited States
CitySeattle, WA

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Visual cluster exploration of web clickstream data'. Together they form a unique fingerprint.

Cite this