In the past decade there has been a growing interest in devising classification algorithms that are applicable to large bodies of data. Such algorithms are characterized necessarily by a sacrifice of statistical sophistication for a gain in computational simplicity. Accordingly, inferences drawn from taxonomic studies in which these algorithms have been employed may be affected by accidental and poorly understood features of such algorithms. An error-analytic technique is presented that reduces this possibility. It is applicable to many of the classification algorithms currently in use. The combinatorial problems encountered in the error analysis are discussed and a computationally viable method for their solution is formulated. The technique is illustrated by an experiment with a small set of data. © 1971.
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