Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
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
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Zonal Co-location Pattern Discovery with Dynamic Parameters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The iZi project: easy prototyping of interesting pattern mining algorithms
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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Spatial data mining has been extensively studied for GIS applications. To deal with a fast increasing of data, investigations for spatial data analysis are needed. In this paper, we propose a spatial data mining approach which adapts the existing colocation concept to characterize soil erosion hazard. In order to manage this task, we put the colocation mining task into a more general framework. Based on this framework, new constraints linked to domain knowledge are pushed into the colocation mining algorithm. Finally, we developed a prototype and lead experiments on real scientific datasets.