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
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
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
Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining
IEEE Transactions on Knowledge and Data Engineering
International Journal of Business Intelligence and Data Mining
An order-clique-based approach for mining maximal co-locations
Information Sciences: an International Journal
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
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
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
A neighborhood-based clustering algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Co-location pattern analysis represents the subsets of spatial events whose instances are found in close geographic proximity. Given a collection of Boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. Key challenges in co-location pattern analysis are modelling of neighbourhood in spatial domain, minimum prevalent threshold to generate collocation patterns and analysing extended spatial objects. We discuss the above key challenges using event centric approach and N-most prevalent co-location patterns approach. We propose a window-based model to find the neighbourhood for point spatial datasets and the multiple window model for extended spatial data objects. We also use N-most prevalent co-location patterns approach to filter the number of co-location pattern generation. We propose a more generic and efficient window-based model algorithm to find colocation patterns. Towards the end, we have done a comparative study of the existing approaches with our proposed approach.