Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Association rule mining: models and algorithms
Association rule mining: models and algorithms
Local pattern discovery in Array-CGH data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Mining balanced patterns in web access data
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
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Frequent itemset mining is a promising approach to the study of genomic profiling data. Here a dataset consists of real numbers describing the relative level in which a clone occurs in human DNA for given patient samples. One can then mine, for example, for sets of samples that share some common behavior on the clones, i.e., gains or losses. Frequent itemsets show promising biological expressiveness, can be computed efficiently, and are very flexible. Their visualization provides the biologist with useful information for the discovery of patterns. Also it turns out that the use of (larger) frequent itemsets tends to filter out noise.