Ordination Algorithm: Difference between revisions
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An [[Ordination Algorithm]] is a [[clustering algorithm]] that [[partially ordered set|orders]] objects that are characterized by values on multiple variables (i.e., [[multivariate object]]s) so that similar objects are near each other and dissimilar objects are farther from each other. | An [[Ordination Algorithm]] is a [[clustering algorithm]] that [[partially ordered set|orders]] objects that are characterized by values on multiple variables (i.e., [[multivariate object]]s) so that similar objects are near each other and dissimilar objects are farther from each other. | ||
* <B>AKA:</B> [[Ordination]]. | |||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** [[principal components analysis (PCA)]]. | ** [[principal components analysis (PCA)]]. | ||
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** [[Bray–Curtis ordination]]. | ** [[Bray–Curtis ordination]]. | ||
** [[redundancy analysis]] (RDA). | ** [[redundancy analysis]] (RDA). | ||
* <B>See:</B> [[Multivariate Analysis]], [[Gradient Analysis]], [[Data Clustering]], [[Exploratory Data Analysis]], [[Hypothesis Testing | * <B>See:</B> [[Multivariate Analysis]], [[Gradient Analysis]], [[Data Clustering]], [[Exploratory Data Analysis]], [[Hypothesis Testing]]. | ||
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Revision as of 15:43, 27 March 2016
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.
- AKA: Ordination.
- 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.
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.