MultiMediaMiner: a system prototype for multimedia data mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Self-Organizing Maps
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Efficient Mining of Spatiotemporal Patterns
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Mining Dynamic Interdimension Association Rules for Local-Scale Weather Prediction
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
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Extracting knowledge and features from a large amount of remote sensing images has become highly required recent years. Spatiotemporal data mining techniques are studied to discover knowledge from these images in order to provide more precise weather prediction. Two learning granularities have been proposed for inductive learning from spatial data: one is spatial object granularity and the other is pixel granularity. In this paper, we propose a pixel granularity based framework to extract useful knowledge from the remote sensing image database by using SOM and association rules mining. A three-stage algorithm, named as Starsi, is also proposed and used in this framework.