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 Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th 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
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
A neighborhood-based clustering algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Can we apply projection based frequent pattern mining paradigm to spatial co-location mining?
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining spatial colocation patterns: a different framework
Data Mining and Knowledge Discovery
A multiple window-based co-location pattern mining approach for various types of spatial data
International Journal of Computer Applications in Technology
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Spatial co-location patterns represent the subsets of Boolean spatial features whose instances often locate in close geographic proximity. The existing co-location pattern mining algorithms aim to find spatial relations based on the distance threshold. However, it is hard to decide the distance threshold for a spatial data set without any prior knowledge. Moreover, spatial data sets are usually not evenly distributed and a single distance value cannot fit an irregularly distributed spatial data set well. In this paper, we propose the notion of the k-nearest features (simply k-NF)-based co-location pattern. The k-NF set of a spatial feature's instances is used to evaluate the spatial relationship between this feature and any other feature. A k-NF-based co-location pattern mining algorithm by using T-tree (KNFCOM-T in short) is further presented to identify the co-location patterns in large spatial data sets. The experimental results show that the KNFCOM-T algorithm is effective and efficient and its complexity is O(n).