Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 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
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Fast mining of spatial collocations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A framework for mining topological patterns in spatio-temporal databases
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Discovering geographical-specific interests from web click data
Proceedings of the first international workshop on Location and the web
Mining Spread Patterns of Spatio-temporal Co-occurrences over Zones
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Mining Spatial Co-location Patterns with Dynamic Neighborhood Constraint
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Answering top-k similar region queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Database research at the National University of Singapore
ACM SIGMOD Record
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Advances in sensing and satellite technologies and the growth of Internet have resulted in the easy accessibility of vast amount of spatial data. Extracting useful knowledge from these data is an important and challenging task, in particular, finding interaction among spatial features. Existing works typically adopt a grid-like approach to transform the continuous spatial space to a discrete space. In this paper, we propose to model the spatial features in a continuous space through the use of influence functions. For each feature type, we build an influence map that captures the distribution of the feature instances. Superimposing the influence maps allows the interaction of the feature types to be quickly determined. Experiments on both synthetic and real world datasets indicate that the proposed approach is scalable and is able to discover patterns that have been missed by existing methods.