Partition based spatial-merge join
SIGMOD '96 Proceedings of the 1996 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
Sort-Merge-Join: An Idea Whose Time Has(h) Passed?
Proceedings of the Tenth International Conference on Data Engineering
Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd 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
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
GREW-A Scalable Frequent Subgraph Discovery Algorithm
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
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Existing level-wise spatial co-location algorithms suffer from generating extra, non-clique candidate instances and thus requires cliqueness checking at every level. In this paper, we propose a novel, spatial co-location mining algorithm which automatically generates co-located spatial features without generating any non-clique candidates at any level. Subsequently our algorithm generates less candidates than other existing level-wise co-location algorithms without losing any information. The benefit of our algorithm has been clearly observed at an earlier stage in the mining process.