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https://t4tutorials.com/support-confidence-minimum-support-frequent-itemset-in-data-mining/
An itemset is frequent if its support is no less than “minimum support threshold”. Minimum support is always supposed according to the choice. You can select any minimum support to decide that the itemset is frequent or not. What is support or absolute support? The absolute number of transactions which contains an itemset. For example ...
https://www.ques10.com/p/9316/apply-the-apriori-algorithm-with-minimum-support-o/
Apply the Apriori algorithm with minimum support of 30% and minimum confidence of 70%, and find all the association rules in the data set
http://www2.cs.uregina.ca/~dbd/cs831/notes/itemsets/itemset_apriori.html
A frequent itemset is an itemset whose support is greater than some user-specified minimum support (denoted L k, where k is the size of the itemset); A candidate itemset is a potentially frequent itemset (denoted C k, where k is the size of the itemset); Apriori Algorithm. A Java applet which combines DIC, Apriori and Probability Based Objected Interestingness Measures can be found here.
https://en.wikipedia.org/wiki/Association_rule_learning
Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Association rule generation is usually split up into two separate steps: A minimum support threshold is applied to find all frequent itemsets in a database.
http://user.it.uu.se/~kostis/Teaching/DM-05/Slides/association1.pdf
Association Rules & Frequent Itemsets All you ever wanted to know about diapers, beers and their correlation! ... – Each itemset in the lattice is a candidate frequent itemset – Count the support of each candidate by scanning the database ... Minimum Support = 3 If every subset is considered, 6C 1 + 6C 2 3 = 41 With support-based pruning,
https://paginas.fe.up.pt/~ec/files_0506/slides/04_AssociationRules.pdf
have minimum support A subset of a frequent itemset must also be a frequent itemset Generate length (k+1) candidate itemsets from length k frequent itemsets, and Test the candidates against DB to determine which are in fact frequent Use the frequent itemsets to generate association rules. Generation is straightforward 18
http://saedsayad.com/association_rules.htm
Association Rules: Association Rules find all sets of items (itemsets) that have support greater than the minimum support and then using the large itemsets to generate the desired rules that have confidence greater than the minimum confidence. The lift of a rule is the ratio of the observed support to that expected if X and Y were independent ...
https://stackoverflow.com/questions/8857635/how-can-i-get-the-frequencies-of-common-itemsets-from-the-apriori-call-in-r
The association rules are derived from frequent itemsets. I'd like to get the most frequent itemsets in the input transactions. Specifically, I'd like to get all itemsets with a given minimum support. The support of an itemset is the ratio of the number of the transactions that contain the itemset to the total number of transactions. Requirements:
http://user.engineering.uiowa.edu/~comp/public/Apriori.pdf
• An itemset satisfies minimum support if the occurrence frequency of the itemset is greater than or equal to min_sup. • If an itemset satisfies minimum support, then it is a frequent itemset. The University of Iowa Intelligent Systems Laboratory Strong Rules • Rules that satisfy both a minimum support
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