Discovering co-located queries in geographic search logs
Proceedings of the first international workshop on Location and the web
Fast spatial co-location mining without cliqueness checking
Proceedings of the 17th ACM conference on Information and knowledge management
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
Optimal candidate generation in spatial co-location mining
Proceedings of the 2009 ACM symposium on Applied Computing
Spatial outlier detection in heterogeneous neighborhoods
Intelligent Data Analysis
Discovery of feature-based hot spots using supervised clustering
Computers & Geosciences
An order-clique-based approach for mining maximal co-locations
Information Sciences: an International Journal
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
Spatial neighborhood based anomaly detection in sensor datasets
Data Mining and Knowledge Discovery
Efficiently mining co-location rules on interval data
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Exploration and comparison of geographic information sources using distance statistics
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatiotemporal neighborhood discovery for sensor data
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
Mining co-distribution patterns for large crime datasets
Expert Systems with Applications: An International Journal
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
Mining co-locations under uncertainty
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For co-location pattern mining, previous studies often emphasize the equal participation of every spatial feature. As a result, interesting patterns involving events with substantially different frequency cannot be captured. In this paper, we address the problem of mining co-location patterns with rare spatial features. Specifically, we first propose a new measure called the maximal participation ratio (maxPR) and show that a co-location pattern with a relatively high maxPR value corresponds to a co-location pattern containing rare spatial events. Furthermore, we identify a weak monotonicity property of the maxPR measure. This property can help to develop an efficient algorithm to mine patterns with high maxPR values. As demonstrated by our experiments, our approach is effective in identifying co-location patterns with rare events, and is efficient and scalable for large-scale data sets.