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https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html
A great and clearly-presented tutorial on the concepts of association rules and the Apriori algorithm, and their roles in market basket analysis. ... Measure 1: Support. This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. ... One drawback of the confidence measure is that it might ...
http://user.engineering.uiowa.edu/~comp/public/Apriori.pdf
output the rule " s => (l-s)" if confidence C of the rule " s => (l-s)" (=support S of l/support S of s) ³ min_conf Step4: The candidate set = Null NO YES The University of Iowa Intelligent Systems Laboratory Apriori Property • Reducing the search space to avoid finding of …
https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/algo_apriori.htm
Any rule with an improvement of less than 1 does not indicate a real cross-selling opportunity, no matter how high its support and confidence, because it actually offers less ability to predict a purchase than does random chance.
https://infocenter.informationbuilders.com/wf80/topic/pubdocs/RStat16/source/topic49.htm
The association rule has three measures that express the degree of confidence in the rule, Support, Confidence, and Lift. For example, you are in a supermarket to buy milk. Based on the analysis, are you more likely to buy apples or cheese in the same transaction than somebody who did not buy milk?
https://www.quora.com/How-do-I-pick-appropriate-support-confidence-value-when-doing-basket-analysis-with-Apriori-algorithm
Jan 25, 2017 · rules <- apriori(data = dataset, parameter = list(support = 0.003, confidence = 0.8)) If you see there are very less rules with this much of confidence, divide confidence by 2 and try again. rules <- apriori(data = dataset, parameter = list(support = 0.003, confidence = 0.4)) Moving Ahead… As per my experience.
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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