Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
Learning to Recognize Volcanoes on Venus
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Self-Organizing Maps
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining pixel evolutions in satellite image time series for agricultural monitoring
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Exploiting efficient parallelism for mining rules in time series data
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Co-occurring cluster mining for damage patterns analysis of a fuel cell
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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Feature extraction and knowledge discovery from a large amount of image data such as remote sensing images have become highly required recent years. In this study, a framework for data mining from a set of time-series images including moving objects was presented. Time-series images are transformed into time-series cluster addresses by using clustering by two-stage SOM (Self-organizing map) and time-dependent association rules were extracted from it. Semantically indexed data and extracted rules are stored in the object-relational database, which allows high-level queries by entering SQL through the user interface. This method was applied to weather satellite cloud images taken by GMS-5 and its usefulness was evaluated.