An order-clique-based approach for mining maximal co-locations
Information Sciences: an International Journal
Mining Spread Patterns of Spatio-temporal Co-occurrences over Zones
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Regional Pattern Discovery in Geo-referenced Datasets Using PCA
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Constrained colocation mining: application to soil erosion characterization
Proceedings of the 2010 ACM Symposium on Applied Computing
Efficiently detecting clusters of mobile objects in the presence of dense noise
Proceedings of the 2010 ACM Symposium on Applied Computing
A clustering-based visualization of colocation patterns
Proceedings of the 15th Symposium on International Database Engineering & Applications
A neighborhood graph based approach to regional co-location pattern discovery: a summary of results
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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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Zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-location patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-location patterns and propose algorithms (Zoloc-Miner) to discover zonal colocation patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform na篓ive alternatives.