Data mining: concepts and techniques
Data mining: concepts and techniques
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
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
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Constraint driven I/O planning and placement for chip-package co-design
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Criminal Cross Correlation Mining and Visualization
PAISI '09 Proceedings of the Pacific Asia Workshop on Intelligence and Security Informatics
Mining top-k and Bottom-k correlative crime patternsthrough graph representations
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Mining co-distribution patterns for large crime datasets
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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As geospatial data grows explosively, there is a great demand for the incorporation of data mining techniques into a geospatial context. Association rules mining is a core technique in data mining and is a solid candidate for the associative analysis of large geospatial databases. In this article, we propose a geospatial knowledge discovery framework for automating the detection of multivariate associations based on a given areal base map. We investigate a series of geospatial preprocessing steps involving data conversion and classification so that the traditional Boolean and quantitative association rules mining can be applied. Our framework has been integrated into GISs using a dynamic link library to allow the automation of both the preprocessing and data mining phases to provide greater ease of use for users. Experiments with real-crime datasets quickly reveal interesting frequent patterns and multivariate associations, which demonstrate the robustness and efficiency of our approach.