Convergence of an EM-type algorithm for spatial clustering
Pattern Recognition Letters
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
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
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
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Data mining is the process of discovering new patterns and relationships in large datasets. However, several studies have shown that general data mining techniques often fail to extract meaningful patterns and relationships from the spatial data owing to the violation of fundamental geospatial principles. In this tutorial, we introduce basic principles behind explicit modeling of spatial and semantic concepts in data mining. In particular, we focus on modeling these concepts in the widely used classification, clustering, and prediction algorithms. Classification is the process of learning a structure or model (from user given inputs) and applying the known model to the new data. Clustering is the process of discovering groups and structures in the data that are "similar," without applying any known structures in the data. Prediction is the process of finding a function that models (explains) the data with least error. One common assumption among all these methods is that the data is independent and identically distributed. Such assumptions do not hold well in spatial data, where spatial dependency and spatial heterogeneity are a norm. In addition, spatial semantics are often ignored by the data mining algorithms. In this tutorial we cover recent advances in explicitly modeling of spatial dependencies and semantic concepts in data mining.