Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal 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
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Zonal Co-location Pattern Discovery with Dynamic Parameters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
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
SSCP: mining statistically significant co-location patterns
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Towards reducing taxicab cruising time using spatio-temporal profitability maps
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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In many application domains, occurrences of related spatial features may exhibit co-location pattern. For example, some disease may be in spatial proximity of certain type of pollution. This paper studies the problem of regional co-locations with arbitrary shapes. Regional co-locations represent regions in which two spatial features exhibit stronger or weaker co-location than that in other regions. Finding regional co-locations of arbitrary shapes is very challenging because: (1) statistical frameworks for mining regional co-location do not exist; and (2) testing all possible arbitrarily shaped regions is computational prohibitive even for very small dataset. In this paper, we propose frequentist and Bayesian frameworks for mining regional co-locations and develop a probabilistic expansion heuristic to find arbitrary shaped regions. Experimental results on synthetic and real world data show that both frequentist method and Bayesian statistical approach can recover the region with arbitrary shapes. Our approaches outperform baseline algorithms in terms of F measure. Bayesian statistical approach is approximately three orders of magnitude faster than the frequentist approach.