Visualizing the structure of the World Wide Web in 3D hyperbolic space
VRML '95 Proceedings of the first symposium on Virtual reality modeling language
Visualizing complex hypermedia networks through multiple hierarchical views
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Constructing community in cyberspace
CHI 98 Cconference Summary on Human Factors in Computing Systems
On disconnected browsing of distributed information
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Usage-based visualization of web localities
APVis '01 Proceedings of the 2001 Asia-Pacific symposium on Information visualisation - Volume 9
Webmining: learning from the world wide web
Computational Statistics & Data Analysis - Nonlinear methods and data mining
INSITE: A Tool for Interpreting Users? Interaction with a Web Space
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
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INsite is a heuristic-based implementation to provide consistent tracking, analysis and visualization of users' interactions with a generic web site. Our research has immediate applicability in such disparate fields as Business, E-commerce, Distance Education, Entertainment and Management for capturing individual and collective profiles of customers, learners and employees. INsite can identify trends and changes in user(s) behavior (interests) by monitoring their online interactions. It has a three-tier architecture for tracking, analysis and visualization. First, a remote agent transparently tracks user-navigation-paths within a site. Second, a unique Connectivity Matrix (CM) Model (a set of Connectivity Matrices) represents each path (and cluster of paths). Third, the user-web site interaction, thus translated to a finite number of CM-Models, is readily visualized by graphically representing the member matrices of the models. Each member matrix of a representative CM-Model captures a single navigational attribute. Our dimensionally static approach to path and cluster representation by the Connectivity Matrices can reduce the complexity of analysis by several orders. Consequently, we employ a new paradigm for dynamic clustering that leverages on the unique CM-Model of representation.