Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Algorithms
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Top-k subgraph matching query in a large graph
Proceedings of the ACM first Ph.D. workshop in CIKM
Mining N-most Interesting Itemsets using Support-Ordered Tries
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
Density based co-location pattern discovery
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Towards region discovery in spatial datasets
PAKDD'08 Proceedings of the 12th 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
On trip planning queries in spatial databases
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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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Recently, there has been considerable interest in mining spatial colocation patterns from large spatial datasets. Spatial colocations represent the subsets of spatial events whose instances are frequently located together in nearby geographic area. Most studies of spatial colocation mining require the specification of a minimum prevalent threshold to find the interesting patterns. However, it is difficult for users to provide appropriate thresholds without prior knowledge about the task-specific spatial data. We propose a different framework for spatial colocation pattern mining: finding N -most prevalent colocated event sets, where N is the desired number of event sets with the highest interest measure values per each pattern size. We developed an algorithm for mining N -most prevalent colocation patterns. Experimental results with real data show that our algorithmic design is computationally effective.