GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Associations in Spatial Data - An Efficient Medoid Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Mining confident co-location rules without a support threshold
Proceedings of the 2003 ACM symposium on Applied computing
Fast mining of spatial collocations
Proceedings of the tenth 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
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
On the Relationships between Clustering and Spatial Co-location Pattern Mining
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
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
Finding N-Most Prevalent Colocated Event Sets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
A large-scale study on map search logs
ACM Transactions on the Web (TWEB)
Detecting nearly duplicated records in location datasets
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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
SSCP: mining statistically significant co-location patterns
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Proceedings of the fifth ACM international conference on Web search and data mining
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
A location based text mining method using ANN for geospatial KDD process
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Cross-Correlation Measure for Mining Spatio-Temporal Patterns
Journal of Database Management
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Co-location pattern discovery is to find classes of spatial objects that are frequently located together. For example, if two categories of businesses often locate together, they might be identified as a co-location pattern; if several biologic species frequently live in nearby places, they might be a co-location pattern. Most existing co-location pattern discovery methods are generate-and-test methods, that is, generate candidates, and test each candidate to determine whether it is a co-location pattern. In the test step, we identify instances of a candidate to obtain its prevalence. In general, instance identification is very costly. In order to reduce the computational cost of identifying instances, we propose a density based approach. We divide objects into partitions and identifying instances in dense partitions first. A dynamic upper bound of the prevalence for a candidate is maintained. If the current upper bound becomes less than a threshold, we stop identifying its instances in the remaining partitions. We prove that our approach is complete and correct in finding co-location patterns. Experimental results on real data sets show that our method outperforms a traditional approach.