Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Spatial autoregression and related spatio-temporal models
Journal of Multivariate Analysis
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
An efficient spatial semi-supervised learning algorithm
International Journal of Parallel, Emergent and Distributed Systems
ACM Computing Surveys (CSUR)
Spatially Adaptive Classification and Active Learning of Multispectral Data with Gaussian Processes
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
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
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The volume of spatiotemporal data being generated from scientific simulations and observations from sensors is growing at an astronomical rate. This data explosion is going to pose three challenges to the existing data mining infrastructure: algorithmic, computational, and I/O. Over the years we have implemented several spatiotemporal data mining algorithms including: outliers/anomalies, colocation patterns, change patterns, clustering, classification, and prediction algorithms. In this paper we briefly discuss the core spatiotemporal pattern mining algorithms along with some of the computational and I/O challenges associated with the big data.