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A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms

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  • Maurice Roux

    (Faculté des Sciences de St-Jérôme)

Abstract

A general scheme for divisive hierarchical clustering algorithms is proposed. It is made of three main steps: first a splitting procedure for the subdivision of clusters into two subclusters, second a local evaluation of the bipartitions resulting from the tentative splits and, third, a formula for determining the node levels of the resulting dendrogram. A set of 12 such algorithms is presented and compared to their agglomerative counterpart (when available). These algorithms are evaluated using the Goodman-Kruskal correlation coefficient. As a global criterion it is an internal goodness-of-fit measure based on the set order induced by the hierarchy compared to the order associated with the given dissimilarities. Applied to a hundred random data tables and to three real life examples, these comparisons are in favor of methods which are based on unusual ratio-type formulas to evaluate the intermediate bipartitions, namely the Silhouette formula, the Dunn's formula and the Mollineda et al. formula. These formulas take into account both the within cluster and the between cluster mean dissimilarities. Their use in divisive algorithms performs very well and slightly better than in their agglomerative counterpart.

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  • Maurice Roux, 2018. "A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 345-366, July.
    • Handle: RePEc:spr:jclass:v:35:y:2018:i:2:d:10.1007_s00357-018-9259-9
    DOI: 10.1007/s00357-018-9259-9
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    References listed on IDEAS

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    1. Gabor J. Szekely & Maria L. Rizzo, 2005. "Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 151-183, September.
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