Abstract:
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.