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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
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
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Knowledge Discovery in Spatial Databases
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and 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
Spatial associative classification at different levels of granularity: a probabilistic approach
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Geo-spatial data mining in the analysis of a demographic database
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
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
International Journal of Business Intelligence and Data Mining
A multi-relational approach to spatial classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding N-Most Prevalent Colocated Event Sets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Towards region discovery in spatial datasets
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A neighborhood-based clustering algorithm
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
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 colocation patterns represent the subsets of spatial events whose instances are often located in close geographic proximity. Most studies of spatial colocation mining require the specification of two parameter constraints to find interesting colocation patterns. One is a minimum prevalent threshold of colocations, and the other is a distance threshold to define spatial neighborhood. However, it is difficult for users to decide appropriate threshold values without prior knowledge of their task-specific spatial data. In this paper, we propose a different framework for spatial colocation pattern mining. To remove the first constraint, we propose the problem of finding N-most prevalent colocated event sets, where N is the desired number of colocated event sets with the highest interest measure values per each pattern size. We developed two alternative algorithms for mining the N-most patterns. They reduce candidate events effectively and use a filter-and-refine strategy for efficiently finding colocation instances from a spatial dataset. We prove the algorithms are correct and complete in finding the N-most prevalent colocation patterns. For the second constraint, a distance threshold for spatial neighborhood determination, we present various methods to estimate appropriate distance bounds from user input data. The result can help an user to set a distance for a conceptualization of spatial neighborhood. Our experimental results with real and synthetic datasets show that our algorithmic design is computationally effective in finding the N-most prevalent colocation patterns. The discovered patterns were different depending on the distance threshold, which shows that it is important to select appropriate neighbor distances.