Ordination Algorithm

An Ordination Algorithm is a clustering algorithm that orders objects that are characterized by values on multiple variables (i.e., multivariate objects) so that similar objects are near each other and dissimilar objects are farther from each other.

• Example(s):
  • principal components analysis (PCA).
  • non-metric multidimensional scaling (NMDS)
  • correspondence analysis (CA) and its derivatives (detrended CA (DCA)
  • canonical CA (CCA)
  • Bray–Curtis ordination.
  • redundancy analysis (RDA).
• See: Multivariate Analysis, Gradient Analysis, Data Clustering, Exploratory Data Analysis, Hypothesis Testing, Partially Ordered Set, Principal Components Analysis, Multidimensional Scaling, Correspondence Analysis, Detrended Correspondence Analysis, [[Bray&Ndash;Curtis Ordination]].

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References

2016

• (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/ordination_(statistics) Retrieved:2016-3-27.
  • In multivariate analysis, ordination or gradient analysis is a method complementary to data clustering, and used mainly in exploratory data analysis (rather than in hypothesis testing). Ordination orders objects that are characterized by values on multiple variables (i.e., multivariate objects) so that similar objects are near each other and dissimilar objects are farther from each other. These relationships between the objects, on each of several axes (one for each variable), are then characterized numerically and/or graphically. Many ordination techniques exist, including principal components analysis (PCA), non-metric multidimensional scaling (NMDS), correspondence analysis (CA) and its derivatives (detrended CA (DCA), canonical CA (CCA)), Bray–Curtis ordination, and redundancy analysis (RDA), among others.

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