Computational geometry: an introduction
Computational geometry: an introduction
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Database Systems (TODS)
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
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
Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining spatial association rules in image databases
Information Sciences: an International Journal
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering geographical-specific interests from web click data
Proceedings of the first international workshop on Location and the web
Discovering co-located queries in geographic search logs
Proceedings of the first international workshop on Location and the web
Density based co-location pattern discovery
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Mining frequent patterns in image databases with 9D-SPA representation
Journal of Systems and Software
Discovery of feature-based hot spots using supervised clustering
Computers & Geosciences
Journal of Intelligent Information Systems
Mining spatial object associations for scientific data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mining correlation between locations using human location history
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Discovering spatial interaction patterns
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
DisIClass: discriminative frequent pattern-based image classification
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Co-location pattern mining for unevenly distributed data: algorithm, experiments and applications
International Journal of Computational Science and Engineering
Mining maximal co-located event sets
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Efficient mining of correlation patterns in spatial point data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Motion-Alert: automatic anomaly detection in massive moving objects
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
Can we apply projection based frequent pattern mining paradigm to spatial co-location mining?
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Regional co-locations of arbitrary shapes
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Mining co-locations under uncertainty
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Spatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. An example of such a pattern can associate contaminated water reservoirs with certain deceases in their spatial neighborhood. Previous work on discovering collocation patterns converts neighborhoods of feature instances to itemsets and applies mining techniques for transactional data to discover the patterns. We propose a method that combines the discovery of spatial neighborhoods with the mining process. Our technique is an extension of a spatial join algorithm that operates on multiple inputs and counts long pattern instances. As demonstrated by experimentation, it yields significant performance improvements compared to previous approaches.