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https://www.quora.com/What-is-support-and-confidence-in-data-mining
T: (A set of) All transactions that customers make and are recorded in the stores system. (Since most of the customers use credit/debit cards, there is a unique number associated to their shopping list.) Basket: A set of all items bought by a customer. Item-set: A set of items that we are interested in.
https://searchcustomerexperience.techtarget.com/answer/Confidence-and-support-in-data-mining
Sep 21, 2001 · Support used in Data Mining/Intelligence These are fairly ubiquitous words in and out of the spaces of DM/BI/mining, but confidence can refer to the anticipated range of an output variable given a set of input variable 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,...
https://www.researchgate.net/publication/233754781_Support_vs_Confidence_in_Association_Rule_Algorithms
Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. A data mining …
https://www.youtube.com/watch?v=iuIEVNhWtlw
Jul 22, 2014 · Association analysis: Frequent Patterns, Support, Confidence and Association Rules - Duration: 19:31. ... Data Mining Lecture - - Finding frequent item sets Apriori Algorithm ...Author: Chris Kimmer
https://en.wikipedia.org/wiki/Lift_(data_mining)
Lift (data mining) In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.
https://docs.oracle.com/cd/B28359_01/datamine.111/b28129/algo_apriori.htm
Support and confidence are also the primary metrics for evaluating the quality of the rules generated by the model. Additionally, Oracle Data Mining supports lift for association rules. These statistical measures can be used to rank the rules and hence the usefulness of the predictions.
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