Automatic classification of plants based on information content

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Abstract
An information theory model is described and its application is illustrated by an actual example. Classification is accomplished in two stages. The first stage includes cluster analysis of a random sample by an agglomerative method. Cluster analysis is followed by nearest neighbor sorting in the second stage whereby the clustering results are imposed on a second random sample of the same collection. The advantage of the procedure resides in the fact that large samples can be handled, and also, the classification produced in the second stage can be used, under specific restrictive assumptions, for unbiased prediction of different population properties. While the present paper is principally concerned with the technique itself, some taxonomic conclusions are also given.

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