Mining confident co-location rules without a support threshold
Proceedings of the 2003 ACM symposium on Applied computing
Spatial contextual noise removal for post classification smoothing of remotely sensed images
Proceedings of the 2005 ACM symposium on Applied computing
The role of visualization in effective data cleaning
Proceedings of the 2005 ACM symposium on Applied computing
Spatial associative classification: propositional vs structural approach
Journal of Intelligent Information Systems
An efficient spatial semi-supervised learning algorithm
International Journal of Parallel, Emergent and Distributed Systems
A Comparative Study on Clustering Algorithms for Multispectral Remote Sensing Image Recognition
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
*Miner: a spatial and spatiotemporal data mining system
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Effective spatial clustering methods for optimal facility establishment
Intelligent Data Analysis
Knowledge and Information Systems
Spatial neighborhood based anomaly detection in sensor datasets
Data Mining and Knowledge Discovery
Effective spatio-temporal analysis of remote sensing data
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Relational mining in spatial domains: accomplishments and challenges
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
AntTrend: stigmergetic discovery of spatial trends
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Mining model trees from spatial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
RETRACTED: Application of Bayes linear discriminant functions in image classification
Pattern Recognition Letters
STPMiner: a highperformance spatiotemporal pattern mining toolbox
Proceedings of the 2nd international workshop on Petascal data analytics: challenges and opportunities
Nature-Inspired approaches to mining trend patterns in spatial databases
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Modeling spatial dependencies and semantic concepts in data mining
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Spatiotemporal data mining in the era of big spatial data: algorithms and applications
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Modeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov random fields (MRF) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression (SAR) model, which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas. We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogous to the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework.