Validation of unsupervised clustering methods for leaf phenotype screening
نویسندگان
چکیده
The assessment of visible differences in leaf shape between plant species or mutants (phenotyping) plays a significant role in plant research. This paper investigates the application of unsupervised data clustering techniques for phenotype screening to find hidden common shape categories. A set of two wildtypes and seven mutations of Arabidopsis acted as a test case. K-Means, NG, GNG, SOM and ART2a were evaluated by classical validity indices and one index derived from the task at hand. K-Means showed the best results and a low agreement between classical validity measures and task constraints was found.
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