On Clustering Validation Techniques
Journal of Intelligent Information Systems
Validation indices for graph clustering
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
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
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Independent component analysis and rough fuzzy based approach to web usage mining
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Validation and interpretation of Web users' sessions clusters
Information Processing and Management: an International Journal
Visualizing web navigation patterns with factor analysis
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
Mining usage web log via independent component analysis and rough fuzzy
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Usage Profile Generation from Web Usage Data Using Hybrid Biclustering Algorithm
International Journal of Applied Evolutionary Computation
Hi-index | 0.00 |
One of the main issues in Web usage mining is the discovery of patterns in the navigational behavior of Web users. Standard approaches, such as clustering of users' sessions and discovering association rules or frequent navigational paths, do not generally allow to characterize or quantify the unobservable factors that lead to common navigational patterns. Therefore, it is necessary to develop techniques that can discover hidden and useful relationships among users as well as between users and Web objects. Correspondence Analysis (CO-AN) is particularly useful in this context, since it can uncover meaningful associations among users and pages. We present a model-based cluster analysis for Web users sessions including a novel visualization and interpretation approach which is based on CO-AN.