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https://en.wikipedia.org/wiki/Apriori_algorithm
Apriori is an algorithm for frequent item set mining and association rule learning over relational databases.It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
https://www3.cs.stonybrook.edu/~cse634/lecture_notes/07apriori.pdf
The Apriori Algorithm: Example • Consider a database, D , consisting of 9 transactions. • Suppose min. support count required is 2 (i.e. min_sup = 2/9 = 22 % ) • Let minimum confidence required is 70%. • We have to first find out the frequent itemset using Apriori algorithm. • Then, Association rules will be generated using min. support &
https://www.softwaretestinghelp.com/apriori-algorithm/
Nov 10, 2019 · #1) In the first iteration of the algorithm, each item is taken as a 1-itemsets candidate. #2) Let there be some minimum support, min_sup ( eg 2). #3) Next, 2-itemset frequent items with min_sup are discovered. #4) The 2-itemset candidates are pruned using min-sup threshold value…
https://www.kdnuggets.com/2016/04/association-rules-apriori-algorithm-tutorial.html
This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. In Table 1 below, the support of {apple} is 4 out of 8, or 50%. Itemsets can also contain multiple items. For instance, the support of {apple, beer, rice} is 2 out of 8, or 25%.
https://stackoverflow.com/questions/2008488/minimum-confidence-and-minimum-support-for-apriori
What are appropriate values for minimum confidence and minimum support values for the Apriori algorithm? How could you tweak them? Are they fixed values, or do they change during the running of the algorithm? If you have used this algorithm before, what values did you use?
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