Ordination Algorithm: Difference between revisions
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An [[Ordination Algorithm]] is a [[ | An [[Ordination Algorithm]] is a [[exploratory data analysis algorithm]] that [[partially ordered set|order]]s 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 Algorithm|Ordination]]. | |||
* <B>Example(s):</B> | * <B>Example(s):</B> | ||
** [[principal components analysis (PCA)]]. | ** [[principal components analysis (PCA)]]. | ||
** non-metric [[multidimensional scaling]] (NMDS) | ** non-metric [[multidimensional scaling]] (NMDS) | ||
** [[correspondence analysis]] (CA) and its derivatives ([[detrended correspondence analysis|detrended CA (DCA)]] | ** [[correspondence analysis]] (CA) and its derivatives ([[detrended correspondence analysis|detrended CA (DCA)]]. | ||
** [[canonical CA (CCA)]] | ** [[canonical CA (CCA)]]. | ||
** [[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>Counter-Example(s):</B> | |||
** [[Data Clustering Algorithm]]. | |||
* <B>See:</B> [[Multivariate Analysis]], [[Gradient Analysis]], [[Data Clustering]], [[Exploratory Data Analysis]], [[Hypothesis Testing]]. | |||
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==References== | |||
== References == | |||
=== 2016 === | === 2016 === | ||
* (Wikipedia, 2016) | * (Wikipedia, 2016) ⇒ http://wikipedia.org/wiki/ordination_(statistics) Retrieved:2016-3-27. | ||
** In [[multivariate analysis]], '''ordination''' or | ** In [[multivariate analysis]], '''ordination''' or <B>[[gradient analysis]]</B> is a method complementary to [[data clustering]], and used mainly in [[exploratory data analysis]] (rather than in [[hypothesis testing]]). Ordination [[partially ordered set|order]]s 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 correspondence analysis|detrended CA (DCA)]], canonical CA (CCA)), [[Bray–Curtis ordination]], and [[redundancy analysis]] (RDA), among others. | ||
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[[Category:Concept]] | [[Category:Concept]] | ||
Latest revision as of 00:46, 19 August 2024
An Ordination Algorithm is a exploratory data analysis 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).
- ...
- Counter-Example(s):
- See: Multivariate Analysis, Gradient Analysis, Data Clustering, Exploratory Data Analysis, Hypothesis Testing.
References
2016
- (Wikipedia, 2016) ⇒ http://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.