Species Associations and Conditional Clustering: Clustering With or Without Pairwise Resemblances
Traditional procedures for clustering objects consist of two steps: measuring pairwise resemblance based on the attributes, and a clustering algorithm. The use of pairwise resemblances can be avoided; a set of objects can be represented as a set of lists of attribute states; an application of the Laplace indifference principle then allows an estimate to be made of the probability of each list as representative of an association of objects. By use of set-covering procedures, the object associations having maximum joint probability are found. The procedure is generalized to multistate unordered and ordered attributes, to frequencies, and to directly obtained relational data.
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