Many real-world e-commerce applications require the mining of large volumes of transaction data to extract marketing and sales information. This paper describes the Directed Association Visualization (DAV) system that visually associates product affinities and relationships for large volumes of e-commerce transaction data. DAV maps transaction data items and their relationships to vertices, edges, and positions on a visual spherical surface. DAV encompasses several innovative techniques (1) items are positioned according to their associations to show the strength of their relationships; (2) edges with arrows are used to represent the implication directions; (3) a mass-spring engine is integrated into a visual data mining platform to provide a self-organized graph. We have applied this system successfully to market basket analysis and e-customer profiling Internet applications.

This paper discusses the visualization of the relationships in e-commerce transactions. To date, many practical research projects have shown the usefulness of a physics-based mass- spring technique to layout data items with close relationships on a graph. We describe a market basket analysis visualization system using this technique. This system is described as the following: (1) integrates a physics-based engine into a visual data mining platform; (2) use a 3D spherical surface to visualize the cluster of related data items; and (3) for large volumes of transactions, uses hidden structures to unclutter the display. Several examples of market basket analysis are also provided.

This paper discusses the visualization of the relationships in e-commerce transactions. To date, many practical research projects have shown the usefulness of a physics-based mass-spring technique to layout data items with close relationships on a graph. We describe a market basket analysis visualization system (MAV) using this technique. This system is described as the following: (1) integrates a physics-based engine into a visual data mining platform; (2) uses a 3D spherical surface to visualize the cluster of related data items; and (3) for large volumes of transactions, uses hidden structures to unclutter the display. Several examples of market basket analysis are also provided.