Clustering and Partitioning

Hierarchical clustering methods and partitioning techniques such as K-means partitioning and two-way indicator species analysis are useful tools for summarising group structure within large, complex, multivariate data-sets that are increasingly common in palaeolimnology. The incorporation of one- or two-dimensional constraints in the clustering algorithms provides means of exploring group structure in temporal, stratigraphical data and in geographical modern data, respectively. Indicator species analysis with its associated permutation tests is a simple and effective means of detecting statistically significant indicator species for any grouping of a set of objects. The newly developed approach of multivariate regression trees combines partitioning and data exploration with regression and data interpretation and modelling.

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