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https://www.quora.com/What-is-support-and-confidence-in-data-mining
Let me give you an example of “frequent pattern mining” in grocery stores. Customers go to Walmart, tesco, Carrefour, you name it, and put everything they want into their baskets and at the end they check out. Let’s agree on a few terms here: * T:...
https://stats.stackexchange.com/questions/229523/association-rules-support-confidence-and-lift
I am trying to mine association rules from my transaction dataset and I have questions regarding the support, confidence and lift of a rule. Assume we have rule like {X} -> {Y} I know that support is P(XY), confidence is P(XY)/P(X) and lift is P(XY)/P(X)P(Y), where the lift is a measurement of independence of X and Y (1 represents independent)
https://www.researchgate.net/publication/233754781_Support_vs_Confidence_in_Association_Rule_Algorithms
According to these descriptions, the support value of an association rule in a data containing N number of transactions is shown in Equation 2 and confidence value is shown in Equation 3.
https://infocenter.informationbuilders.com/wf80/topic/pubdocs/RStat16/source/topic49.htm
The If part of the rule (the {A} above) is known as the antecedent and the THEN part of the rule is known as the consequent (the {B} above). The antecedent is the condition and the consequent is the result. The association rule has three measures that express the degree of confidence in the rule, Support, Confidence, and Lift.
https://www.solver.com/xlminer/help/association-rules
The first number is called the support for the rule. The support is simply the number of transactions that include all items in the antecedent and consequent parts of the rule. The support is sometimes expressed as a percentage of the total number of records in the database.) The other number is known as the confidence of the rule. Confidence ...
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
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. ... The {beer -> soda} rule has the highest confidence at 20% ...
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