Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Plane-Sweep Algorithm for the All-Nearest-Neighbors Problem for a Set of Convex Planar Objects
WADS '93 Proceedings of the Third Workshop on Algorithms and Data Structures
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
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Memory-adative association rules mining
Information Systems - Databases: Creation, management and utilization
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A Join-Less Approach for Co-Location Pattern Mining: A Summary of Results
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
An order-clique-based approach for mining maximal co-locations
Information Sciences: an International Journal
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We consider the problem of executing collocation pattern queries in limited memory environments. Our experiments show that if the memory size is not sufficient to hold all internal data structures used by the iCPI-tree algorithm, its performance decreases dramatically. We present a new method to efficiently process collocation pattern queries using materialized, improved candidate pattern instance tree. We have implemented and tested the aforementioned solution and shown that it can significantly improve the performance of the iCPI-tree algorithm.