Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising
Data Mining and Knowledge Discovery
Data Mining for Measuring and Improving the Success of Web Sites
Data Mining and Knowledge Discovery
Web page clustering using a self-organizing map of user navigation patterns
Decision Support Systems - Special issue: Web data mining
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
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
GHIC: A Hierarchical Pattern-Based Clustering Algorithm for Grouping Web Transactions
IEEE Transactions on Knowledge and Data Engineering
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A divergence-oriented approach for web users clustering
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
An overview of web data clustering practices
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
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This paper introduces a new algorithm for clustering sequential data. The SKM algorithm is a K-Means-type algorithm suited for identifying groups of objects with similar trajectories and dynamics. We provide a simulation study to show the good properties of the SKM algorithm. Moreover, a real application to website users' search patterns shows its usefulness in identifying groups with heterogeneous behavior. We identify two distinct clusters with different styles of website search.