On the Efficiency and Sensitivity of a Pyramidal Classification Algorithm
Àngel J. Gil
In this paper we propose a Pyramidal Classification Algorithm, which together with an appropriate aggregation index produces an indexed pseudo-hierarchy (in the strict sense) without inversions nor crossings. The computer implementation of the algorithm makes it possible to carry out some simulation tests by Monte Carlo methods in order to study the efficiency and sensitivity of the pyramidal methods of the Maximum, Minimum and UPGMA. The results shown in this paper may help to choose between the three classification methods proposed, in order to obtain the classification that best fits the original structure of the population, provided we have an a priori information concerning this structure.
Pyramidal classification methods, aggregation index, pseudo-hierarchy, Robinsonian dissimilarity, Monte Carlo evaluation, overlapping clusters
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