Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
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
Mining co-location patterns from large spatial datasets
Mining co-location patterns from large spatial datasets
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The present works are focusing on the discovery of global co-location patterns. It is a challenging problem to find the local co-location patterns. A novel method was presented to find local co-location patterns in an event sequence. The local co-location patterns were found by using an effective multi-layer index in a given time window (win) and local neighbor domain set. The experiment was done to prove algorithm effective and feasible.