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When the percentage values of support and confidence is given how can I find the minimum support in Apriori algorithm. For an example when support and confidence is given as 60% and 60% respectively what is the minimum support?
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The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics.
If the association rule is (2,5) -> (3), than is X = (2,5) and Y = (3). The confidence of an association rule is the support of (X U Y) divided by the support of X. Therefore, the confidence of the association rule is in this case the support of (2,5,3) divided by the support of (2,5).
Minimum antecedent support. You can specify a support criterion for keeping rules in the rule set. Support refers to the percentage of records in the training data for which the antecedents (the "if" part of the rule) are true.
A lift value greater than 1 means that item Y is likely to be bought if item X is bought, while a value less than 1 means that item Y is unlikely to be bought if item X is bought.
Minimum support count is the % of the all transaction.suppose you have 60% support count and 5 is the total transaction then in number the min_support will be 5*60/100=3.
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I'm not sure your question makes sense. From your example if you have at least one rule returned with Support and Confidence of 60% you can be sure that the Minimum-Support is at least 60% but it could be more.
Minimum-Support is a parameter supplied to the Apriori algorithm in order to prune candidate rules by specifying a minimum lower bound for the Support measure of resulting association rules. There is a corresponding Minimum-Confidence pruning parameter as well.
Each rule produced by the algorithm has it's own Support and Confidence measures. Roughly, Support is the ratio of instances for which the rule is true out of all instances. Confidence is the ratio of instances for which the rule is true out of the number of instances for which the antecedent (LHS of the implication) is true.
Check out Wikipedia for more rigorous definitions.
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