Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining frequent geographic patterns with knowledge constraints
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
A Framework for Regional Association Rule Mining in Spatial Datasets
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
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
On Regional Association Rule Scoping
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
International Journal of Geographical Information Science
Discovery of interesting regions in spatial data sets using supervised clustering
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Interestingness is not a dichotomy: introducing softness in constrained pattern mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A neighborhood graph based approach to regional co-location pattern discovery: a summary of results
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The motivation for regional association rule mining and scoping is driven by the facts that global statistics seldom provide useful insight and that most relationships in spatial datasets are geographically regional, rather than global. Furthermore, when using traditional association rule mining, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support. In this paper, we systematically study this problem and address the unique challenges of regional association mining and scoping: (1) region discovery: how to identify interesting regions from which novel and useful regional association rules can be extracted; (2) regional association rule scoping: how to determine the scope of regional association rules. We investigate the duality between regional association rules and regions where the associations are valid: interesting regions are identified to seek novel regional patterns, and a regional pattern has a scope of a set of regions in which the pattern is valid. In particular, we present a reward-based region discovery framework that employs a divisive grid-based supervised clustering for region discovery. We evaluate our approach in a real-world case study to identify spatial risk patterns from arsenic in the Texas water supply. Our experimental results confirm and validate research results in the study of arsenic contamination, and our work leads to the discovery of novel findings to be further explored by domain scientists.