Data mining and the Web: past, present and future
Proceedings of the 2nd international workshop on Web information and data management
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Implicit user profiling for on demand relevance feedback
Proceedings of the 9th international conference on Intelligent user interfaces
Using ontologies in personalized mobile applications
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Managing spatial knowledge for mobile personalized applications
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
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There has been a vast increase in the amount of spatial data that has become accessible in recent years, mirroring the continuing explosion in information available online. People browsing the Web can download maps of almost any region when planning trips or seeking directions. However, GIS applications generating maps typically present default maps to clients without personalizing any spatial content. This gives rise to a problem whereby the most relevant map information can be obscured by extraneous spatial data, thus hindering users in achieving map interaction goals. Several applications exist that deliver personalized information, but they rely on clients providing explicit input. We describe a novel system that provides personalized map content using techniques prevalent in data mining to model spatial data interaction and to present users with automatically and implicitly personalized map content. Modeling spatial content preferences in this manner allows us to recommend spatial content to individuals whenever they request maps, without requiring the additional burden of explicit user modeling input.