Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Using Association Rules as Texture Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Association Rules Based on Image Content
ADL '99 Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries
Clustering web images using association rules, interestingness measures, and hypergraph partitions
ICWE '06 Proceedings of the 6th international conference on Web engineering
Towards symbolic mining of images with association rules: Preliminary results on textures
Intelligent Data Analysis - Analysis of Symbolic and Spatial Data
An E-infrastructure to Support Collaborative Embryo Research
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
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We analyse data from the Edinburgh Mouse Atlas Gene-Expression Database (EMAGE) which is a high quality data source for spatio-temporal gene expression patterns. Using a novel process whereby generated patterns are used to probe spatially-mapped gene expression domains, we are able to get unbiased results as opposed to using annotations based predefined anatomy regions. We describe two processes to form association rules based on spatial configurations, one that associates spatial regions, the other associates genes.