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Hyperclust: A Method for Finding Significant Hierarchical and Clinal Structure

Jonathan L. Wong and Roger I. C. Hansell
Systematic Zoology
Vol. 32, No. 3 (Sep., 1983), pp. 239-247
DOI: 10.2307/2413444
Stable URL: http://www.jstor.org/stable/2413444
Page Count: 9
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Hyperclust: A Method for Finding Significant Hierarchical and Clinal Structure
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Abstract

Hyperclust is a new clustering method with which we search for hierarchical structure in binary data. These hierarchical trees have the following properties. Any two objects (branches), that are joined at a node, share common characters in a manner consistent with a random allocation model. This model uses a character pool which is explicitly defined for every node. The test statistic (number of characters shared) follows a hypergeometric probability distribution. Furthermore alternative random allocation models can be tested by using different character pools. Finally by constructing alternative trees incorporating overlapping subsets of objects, we can test whether the local structure within those subsets can be adequately described by a hierarchical model.

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