A method for clustering decision trees that in one embodiment can beimplemented in agenetics-based data classifier for the purpose of speeding up theclassification processand increasing the classification accuracy. The present invention relates to adecision treeclustering method whereby, in order the increase the classification speed andaccuracy ofa data classifier using groups of decision trees, similar decision trees areidentified andclustered.When the method is presented with a group of decision trees encoding in eachof theirleaf nodes a class from the same group of classes, the method identifiessimilar decisiontrees where two decision trees are said to be similar if all the datainstances correctlyclassified by a first decision tree are included in the set of data instancescorrectlyclassified by a second decision tree, in which case the second decision treeis said to begreater or equal than the first decision tree. The clustering is performed byplacing adecision tree in the same cluster with another decision tree that is greateror equal to it,and the process is repeated until no more clustering is possible.
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