Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
ACM Computing Surveys (CSUR)
Information navigation on the web by clustering and summarizing query results
Information Processing and Management: an International Journal
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Combining evidence for automatic web session identification
Information Processing and Management: an International Journal - Issues of context in information retrieval
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
INSITE: A Tool for Interpreting Users? Interaction with a Web Space
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Clustering Web Sessions by Sequence Alignment
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
An Empirical Study on the Visual Cluster Validation Method with Fastmap
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
A Cube Model and Cluster Analysis for Web Access Sessions
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Web page clustering using a self-organizing map of user navigation patterns
Decision Support Systems - Special issue: Web data mining
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
Validating and Refining Clusters via Visual Rendering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Lessons and Challenges from Mining Retail E-Commerce Data
Machine Learning
Dynamic web log session identification with statistical language models
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
Insight and perspectives for content delivery networks
Communications of the ACM - Personal information management
Modeling Online Browsing and Path Analysis Using Clickstream Data
Marketing Science
Model-Based cluster analysis for web users sessions
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
A clustering-based prefetching scheme on a Web cache environment
Computers and Electrical Engineering
A fuzzy bi-clustering approach to correlate web users and pages
International Journal of Knowledge and Web Intelligence
A novel prediction model based on hierarchical characteristic of web site
Expert Systems with Applications: An International Journal
The role of atmospheric cues in online impulse-buying behavior
Electronic Commerce Research and Applications
| Hi-index | 0.00 |
Understanding users' navigation on the Web is important towards improving the quality of information and the speed of accessing large-scale Web data sources. Clustering of users' navigation into sessions has been proposed in order to identify patterns and similarities which are then managed in the context of Web users oriented applications (searching, e-commerce, etc.). This paper deals with the problem of assessing the quality of user session clusters in order to make inferences regarding the users' navigation behavior. A common model-based clustering algorithm is used to result in clusters of Web users' sessions. These clusters are validated by using a statistical test, which measures the distances of the clusters' distributions to infer their dissimilarity and distinguishing level. Furthermore, a visualization method is proposed in order to interpret the relation between clusters. Using real data sets, we illustrate how the proposed analysis can be applied in popular application scenarios to reveal valuable associations among Web users' navigation sessions.