Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Optimized Gain Rules for Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining confident co-location rules without a support threshold
Proceedings of the 2003 ACM symposium on Applied computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A framework for mining topological patterns in spatio-temporal databases
Proceedings of the 14th ACM international conference on Information and knowledge management
Discovery of feature-based hot spots using supervised clustering
Computers & Geosciences
Mining Spatial Co-location Patterns with Dynamic Neighborhood Constraint
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Mining spatial object associations for scientific data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A language and a visual interface to specify complex spatial patterns
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining co-locations under uncertainty
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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This paper describes the need for mining complex relationshipsin spatial data. Complex relationships are definedas those involving two or more of: multi-feature colocation,self-colocation, one-to-many relationships, self-exclusionand multi-feature exclusion. We demonstrate that even inthe mining of simple relationships, knowledge of complexrelationships is necessary to accurately calculate the significanceof results. We implement a representation of spatialdata such that it contains known 'weak-monotonic' properties,which are exploited for the efficient mining of complexrelationships, and discuss the strengths and limitations ofthis representation.