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https://infocenter.informationbuilders.com/wf80/topic/pubdocs/RStat16/source/topic49.htm
Values in the item and confidence columns are the results of the Market Basket Analysis routine. In other words, item is the product recommendation that the customer is most likely to buy after buying item 2 and item 3 together according to …
https://webfocusinfocenter.informationbuilders.com/wfappent/TLs/TL_rstat/source/marketbasket49.htm
Values in the item and confidence columns are the results of the Market Basket Analysis routine. In other words, item is the product recommendation that the customer is most likely to buy after buying item 2 and item 3 together according to …
https://www.youtube.com/watch?v=1D2um4u-BeM
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https://www.analyticsvidhya.com/blog/2014/08/effective-cross-selling-market-basket-analysis/
Aug 04, 2014 · A Marketer would consider rules with high Lift ratio, high Confidence and good support. For example, IF flour, root vegetables & whipped/sour cream are purchased THEN whole milk is also purchased. This rule has 100% confidence. The confidence of 100% tells us that this rule appears to be a very promising rule for the business.
https://www.quora.com/How-do-I-pick-appropriate-support-confidence-value-when-doing-basket-analysis-with-Apriori-algorithm
Jan 25, 2017 · A support / confidence couple can be used for chosing the best rules. However, here is the paper of Agrawal (he first presented this algorithm for the basket market analysis problem) . It is called : Mining the Most Interesting Rules and there you...
https://www.quora.com/What-is-support-and-confidence-in-data-mining
If you consider a basket containing 10 items(5-apples, 3-eggs, 2-pens) then support of any precise item say apple can be 5 as mentioned. Likewise precise value can be calculated by the proportion of number of occurrences to the total number of items in the basket (i.e., support(apples) = 5/8). Confidence : This explai...
https://en.wikipedia.org/wiki/Association_rule_learning
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for …
https://www.academia.edu/648890/Support_vs_Confidence_in_Association_Rule_Algorithms
The discovery of interesting association relationships among large amounts of business transactions is currently vital for making appropriate business decisions. There are currently a variety of algorithms to discover association rules. Some of these
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