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What are the measures of association rules?
An association rule is a condition of the form of X → Y where X ⊆ I and Y ⊆ I are two sets of items. The support of a rule X → Y is the number of transactions that contain both X and Y, while the confidence of a rule X → Y is the number of transactions containing X, that also contain Y.
What is an association rule give example?
A classic example of association rule mining refers to a relationship between diapers and beers. The example, which seems to be fictional, claims that men who go to a store to buy diapers are also likely to buy beer. Of those, about 3,500 transactions, 1.75%, include both the purchase of diapers and beer.
What is association rules coverage?
Coverage (also called cover or LHS-support) is the support of the left-hand-side of the rule, i.e., supp(X). It represents a measure of to how often the rule can be applied. Coverage is quickly calculated from the rules quality measures (support and confidence) stored in the quality slot.
Which are the two measures of rule interestingness of association rules?
Typical objective measures of interestingness include statistical measures like confidence, support (Agrawal et al 1993), lift (Piatetsky- Shapiroet al2000), conviction (Brinet al1997a), rule interest (Brinet al1997a) and collective strength (Aggarwal & Yu 2001).
What is minimum support count?
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.
What is Rule coverage and accuracy?
– Coverage: fraction of records. that satisfy the antecedent of a. rule. – Accuracy: fraction of records. covered by the rule that belong.
Which is an example of the association rule?
Support Count () – Frequency of occurrence of a itemset. Frequent Itemset – An itemset whose support is greater than or equal to minsup threshold. Association Rule – An implication expression of the form X -> Y, where X and Y are any 2 itemsets.
How to calculate the confidence for an association rule?
The confidence for an association rule having a very frequent consequent will always be high. I will introduce some numbers here to clarify this further. Total transactions = 100. 10 of them have both milk and toothbrush, 70 have milk but no toothbrush and 4 have toothbrush but no milk. Consider the numbers from figure on the left.
How is association rule used in data mining?
Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs in a transaction.
What does support mean in the association rule?
Support (s) – The number of transactions that include items in the {X} and {Y} parts of the rule as a percentage of the total number of transaction.It is a measure of how frequently the collection of items occur together as a percentage of all transactions. Support = (X+Y) total –