Evaluating Web Access Log Mining Algorithms: A Cognitive Approach
WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A Web page prediction model based on click-stream tree representation of user behavior
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Web usage mining based on probabilistic latent semantic analysis
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Breast cancer diagnosis using genetic programming generated feature
Pattern Recognition
Information Foraging Theory: Adaptive Interaction with Information
Information Foraging Theory: Adaptive Interaction with Information
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
A comparative study of ontology based term similarity measures on PubMed document clustering
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Evolutionary Computation for Modeling and Optimization
Evolutionary Computation for Modeling and Optimization
Ontology-Based rummaging mechanisms for the interpretation of web usage patterns
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
| Hi-index | 0.00 |
Semantically enriched web usage data have high dimensionality when represented as fixed-length vectors which impairs performance of many data mining algorithms as well as the comprehensibility for a human analyst. The work presented here introduces visitor profile as a set of low-dimensional fixed-length vectors extracted from clickstream of an individual visitor. The usability of this representation for common web usage mining tasks is demonstrated on association rule mining and clustering experiments. Since the availability of reliable pageview weights has been found of critical importance, a supervised algorithm based on genetic programming is proposed for learning these weights from clickstreams of converted visitors. Extraction of features from referring search engine queries is outlined for further work.