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https://infocenter.informationbuilders.com/wf80/topic/pubdocs/RStat16/source/topic49.htm
This is a summary of the descriptive statistics of the distribution values for Support, Confidence, and Lift. Summary of the execution of the apriori commands. This is a summary of the settings that come with the apriori algorithm. Except for Support and Confidence, which you can change in the GUI, the remaining settings are set to default values.
https://stats.stackexchange.com/questions/229523/association-rules-support-confidence-and-lift
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) However, I just don't know how to interpret rules with these indicators. I have rules with high support, high confidence and low lift, is that a good rule ?
https://en.wikipedia.org/wiki/Lift_(data_mining)
If the lift is > 1, like it is here for Rules 1 and 2, that lets us know the degree to which those two occurrences are dependent on one another, and makes those rules potentially useful for predicting the consequent in future data sets. Observe that even though Rule 1 has higher confidence, it has lower lift.
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://www.solver.com/xlminer/help/association-rules
Lift is one more parameter of interest in the association analysis. Lift is nothing but the ratio of Confidence to Expected Confidence. Using the above example, expected Confidence in this case means, "confidence, if buying A and B does not enhance the probability of buying C."
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. ... One drawback of the confidence measure is that it might misrepresent the importance of an association. ... Larger circles imply higher support, while red circles imply higher lift: Associations ...
https://select-statistics.co.uk/blog/market-basket-analysis-understanding-customer-behaviour/
Using the arulesViz package, we plot the rules by confidence, support and lift in Figure 2. This plot illustrates the relationship between the different metrics. It has been shown that the optimal rules are those that lie on what’s known as the “support-confidence boundary”.
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