Market Basket Analysis Task: Difference between revisions
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A [[Market Basket Analysis Task]] is a [[affinity analysis task]] that is based on [[association rule mining]]. | |||
* <B>AKA:</B> [[Market Basket Analysis Task|Basket Analysis]]. | |||
* <B>Context:</B> | |||
** It can be solved by a [[Market Basket Analysis System]] (that implements a [[market basket analysis algorithm]]). | |||
** … | |||
* <B>Example(s):</B> | |||
** [[Data Analysis Task]], | |||
** [[Information Extraction Task]], | |||
** [[Knowledge Discovery Task]]. | |||
* <B>See:</B> [[Discounts And Allowances]], [[Cross-Selling]], [[Up-Selling]], [[Sales Promotion]], [[Apriori Algorithm]]; [[Association Rule]]; [[Frequent Itemset]]; [[Frequent Pattern]]. | |||
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== References == | |||
=== 2017a === | |||
* (Toivonen, 2017) ⇒ Toivonen H. (2017) [https://link.springer.com/referenceworkentry/10.1007/978-1-4899-7687-1_926 Basket Analysis]. In: [[Sammut, C.]], [[Webb, G.I.]] (eds) [https://link.springer.com/referencework/10.1007/978-1-4899-7687-1 Encyclopedia of Machine Learning and Data Mining]. Springer, Boston, MA | |||
** QUOTE: The goal of [[Market Basket Analysis Task|basket analysis]] is to utilize [[large volume]]s of [[electronic receipt]]s, stored at the [[checkout terminal]]s of [[supermarket]]s, for better understanding of [[customer behavior]]. <P> While many forms of [[learning]] and [[mining]] can be applied to [[market basket]]s, the term usually refers to some variant of [[association rule mining]]. In the basic setting, each [[market basket]] constitutes an example essentially defined by the set of purchased products. [[Association rule]]s then identify sets of items that tend to be bought together. A classical, [[anecdotal discovery]] from [[supermarket data]] is that “if a basket contains diapers then it often also contains beer.” This example illustrates several potential benefits of [[Market Basket Analysis Task|market basket analysis]] by [[association rule]]s: simplicity and understandability of the results, actionability of the results, and a form of [[Unsupervised Machine Learning|nonsupervised]] approach where the consequent of the rule has not been fixed by the user. [[Association rule]]s are often found with the [[Apriori algorithm]], and are based on [[frequent itemset]]s. | |||
=== 2017 === | |||
* (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Affinity_analysis Retrieved:2017-12-1. | |||
** .. In retail, affinity analysis is used to perform '''market basket analysis''', in which retailers seek to understand the purchase behavior of customers. This information can then be used for purposes of [[cross-selling]] and [[up-selling]], in addition to influencing [[sales promotion]]s, loyalty programs, store design, and [[Discounts and allowances|discount plans]].<ref name="demystifyingmba">[http://www.information-management.com/specialreports/20061031/1067598-1.html "Demystifying Market Basket Analysi"]. Retrieved 3 November 2009.</ref> | |||
<references/> | |||
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__NOTOC__ | |||
[[Category:Concept]] | |||
[[Category:Machine Learning]] |
Latest revision as of 09:06, 23 May 2024
A Market Basket Analysis Task is a affinity analysis task that is based on association rule mining.
- AKA: Basket Analysis.
- Context:
- It can be solved by a Market Basket Analysis System (that implements a market basket analysis algorithm).
- …
- Example(s):
- See: Discounts And Allowances, Cross-Selling, Up-Selling, Sales Promotion, Apriori Algorithm; Association Rule; Frequent Itemset; Frequent Pattern.
References
2017a
- (Toivonen, 2017) ⇒ Toivonen H. (2017) Basket Analysis. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
- QUOTE: The goal of basket analysis is to utilize large volumes of electronic receipts, stored at the checkout terminals of supermarkets, for better understanding of customer behavior.
While many forms of learning and mining can be applied to market baskets, the term usually refers to some variant of association rule mining. In the basic setting, each market basket constitutes an example essentially defined by the set of purchased products. Association rules then identify sets of items that tend to be bought together. A classical, anecdotal discovery from supermarket data is that “if a basket contains diapers then it often also contains beer.” This example illustrates several potential benefits of market basket analysis by association rules: simplicity and understandability of the results, actionability of the results, and a form of nonsupervised approach where the consequent of the rule has not been fixed by the user. Association rules are often found with the Apriori algorithm, and are based on frequent itemsets.
- QUOTE: The goal of basket analysis is to utilize large volumes of electronic receipts, stored at the checkout terminals of supermarkets, for better understanding of customer behavior.
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Affinity_analysis Retrieved:2017-12-1.
- .. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers. This information can then be used for purposes of cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans.[1]
- ↑ "Demystifying Market Basket Analysi". Retrieved 3 November 2009.