Scalable Sweeping-Based Spatial Join
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Evaluating Attraction in Spatial Point Patterns with an Application in the Field of Cultural History
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Mining of Complex Spatial Co-location Patterns Using GLIMIT
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Density based co-location pattern discovery
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Discovering spatial interaction patterns
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Inferring geographic coincidence in ephemeral social networks
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
A multiple window-based co-location pattern mining approach for various types of spatial data
International Journal of Computer Applications in Technology
Regional co-locations of arbitrary shapes
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
| Hi-index | 0.00 |
Spatial co-location pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose events are frequently located together in geographic space. However, previous research literatures for mining co-location patterns assume a static neighborhood constraint that apparently introduces many drawbacks. In this paper, we conclude the preferences that algorithms rely on when making decisions for mining co-location patterns with dynamic neighborhood constraint. Based on this, we define the mining task as an optimization problem and propose a greedy algorithm for mining co-location patterns with dynamic neighborhood constraint. The experimental evaluation on a real world data set shows that our algorithm has a better capability than the previous approach on finding co-location patterns together with the consideration of the distribution of data set.