Recherche de règles d'association - Historique des versions

Pitpitt : Remplacement de texte : « ↵ » par «  ==Sources== »

2024-01-28T16:48:02Z

Remplacement de texte : « ↵<small> » par «  ==Sources== »

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	~~<small>~~		==Sources==

Pitpitt le 24 mai 2021 à 20:28

2021-05-24T20:28:18Z

← Version précédente		Version du 24 mai 2021 à 16:28
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	[https://en.wikipedia.org/wiki/Association_rule_learning Source : Wikipedia (Association rule learning)]		[https://en.wikipedia.org/wiki/Association_rule_learning Source : Wikipedia (Association rule learning)]
			[[Catégorie:GRAND LEXIQUE FRANÇAIS]]
	[[Catégorie:~~Intelligence artificielle]]~~
	~~[[Catégorie:publication~~]]

Imeziani le 24 mai 2021 à 15:54

2021-05-24T15:54:18Z

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	[[Catégorie:Intelligence artificielle]]		[[Catégorie:Intelligence artificielle]]
	[[Catégorie:~~Révision~~]]		[[Catégorie:publication]]

JBM : JBM a déplacé la page Association Rule Learning vers Recherche de règles d'association

2021-05-24T15:33:07Z

JBM a déplacé la page Association Rule Learning vers Recherche de règles d'association

← Version précédente	Version du 24 mai 2021 à 11:33
(Aucune différence)

JBM le 24 mai 2021 à 15:32

2021-05-24T15:32:42Z

← Version précédente		Version du 24 mai 2021 à 11:32
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	== ~~en construction~~ ==		== Définition ==
	[[~~Catégorie:Intelligence artificielle~~]]		Technique d'[[apprentissage automatique]] qui permet la découverte de [[règles]] intelligibles et exploitables, qui expriment des associations entre des items ou des attributs, dans un [[ensemble de données]] volumineux.
	[[~~Catégorie:vocabulary~~]]

			== Français ==
			'''recherche de règles d'association'''

	~~== Définition ==~~		'''recherche des règles d'association'''
	~~...~~
	~~== Français ==~~
	~~...~~

	== Anglais ==		== Anglais ==
	'''~~Association Rule Learning~~'''		'''association rule learning'''

	discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B \| A), P(C \| A, B), and so forth.
			<small>


	~~<small>~~		[https://hal.archives-ouvertes.fr/tel-01466790/file/These.pdf Source : Parfait Bemarisika. ''Extraction de règles d’association selon le couple support-MGK : Graphes implicatifs et Applications en didactique des mathématiques''. Université d’Antananarivo, 2016. fftel-01466790 ]

	~~Machine Learning for Beginners – a How-to Guide~~		[https://en.wikipedia.org/wiki/Association_rule_learning Source : Wikipedia (Association rule learning)]
	https://~~opendatascience~~.~~com~~/~~machine-~~learning~~-for-beginners/~~

	[~~https~~:~~//deeplearning.lipingyang.org/wp-content/uploads/2016/12/Glossary-of-Terms-Journal-of-Machine-Learning.pdf Source~~ : ~~Journal of Machine Learning~~ ]		[[Catégorie:Intelligence artificielle]]
			[[Catégorie:Révision]]

Pitpitt le 17 décembre 2020 à 13:27

2020-12-17T13:27:22Z

← Version précédente		Version du 17 décembre 2020 à 09:27
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	Machine Learning for Beginners – a How-to Guide		Machine Learning for Beginners – a How-to Guide
	https://opendatascience.com/machine-learning-for-beginners/		https://opendatascience.com/machine-learning-for-beginners/

			[https://deeplearning.lipingyang.org/wp-content/uploads/2016/12/Glossary-of-Terms-Journal-of-Machine-Learning.pdf Source : Journal of Machine Learning ]

Pitpitt le 17 septembre 2019 à 13:12

2019-09-17T13:12:20Z

← Version précédente		Version du 17 septembre 2019 à 09:12
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	[[Catégorie:Intelligence artificielle]]		[[Catégorie:Intelligence artificielle]]
	[[Catégorie:vocabulary]]		[[Catégorie:vocabulary]]



	== Définition ==		== Définition ==
			...
	== Français ==		== Français ==
	~~'''XXXXXXXXXXXXXXX '''~~		...

	== Anglais ==		== Anglais ==
	'''~~XXXXXXXXXXXXXXX~~ '''		'''Association Rule Learning'''

	discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B \| A), P(C \| A, B), and so forth.		discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B \| A), P(C \| A, B), and so forth.

Pitpitt le 17 août 2019 à 02:48

2019-08-17T02:48:40Z

@@ Ligne 1 : / Ligne 1 : @@
 discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B | A), P(C | A, B), and so forth.
 Machine Learning for Beginners – a How-to Guide
 https://opendatascience.com/machine-learning-for-beginners/

Gdpelletier : Page créée avec « discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is... »

2019-03-08T10:21:48Z

Page créée avec « discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is... »

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discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B | A), P(C | A, B), and so forth.

Machine Learning for Beginners – a How-to Guide
https://opendatascience.com/machine-learning-for-beginners/