The information system as a competitive weapon
Communications of the ACM - Special section on management of information systems
Data Mining for Measuring and Improving the Success of Web Sites
Data Mining and Knowledge Discovery
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Building and Exploiting Ad Hoc Concept Hierarchies for Web Log Analysis
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
WUM - A Tool for WWW Ulitization Analysis
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Analysis and Visualization of Metrics for Online Merchandising
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
User-Driven Navigation Pattern Discovery from Internet Data
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
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The incorporation and exploitation of background knowledge in KDD is essential for the effective discovery of useful patterns and the elimination of trivial results. We observe background knowledge as a combination of beliefs and interestingness measures. In conventional data mining, background knowledge refers to the preferences and properties of the population under observation. In applications analysing the interaction of persons with a system, we identify one additional type of background knowledge, namely about the strategies encountered in pursuit of the interaction objectives. We propose a framework for the modelling of this type of background knowledge and use a template-based mining language to exploit it during the data mining process. We apply our framework on Web usage mining for Web marketing applications.