What is association rule with 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. Data that would point to that might look like this: A supermarket has 200,000 customer transactions.

What are the measures of association rule?

Association rules are rules of the form X -> Y where X (the antecedent or LHS) and Y (the consequent or RHS) are distinct itemsets and where finding Y in transactions which also contain X is probable. The probability is controlled by measures of interest which can be used as constraints to mine association rules.

What are the three measures used in association rules?

Some popular measures are:

• All-confidence.
• Collective strength.
• Leverage.

What is the logic behind association rule?

Association rules corresponding to general relation of two Boolean attributes are introduced. It is shown that the question if the given association rule logically follows from an other given association rule can be converted into the question if suitable formulae of propositional calculus are tautologies.

What is strong association rule?

1. An association rule having support and confidence greater than or equal to a user-specified minimum support threshold and respectively a minimum confidence threshold.

What is an association model?

Association models allow you to predict which items are most likely to appear together, and predict the strength of the relationship between them.

What is coverage association rule?

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.

What is confidence in association rule?

The confidence of an association rule is a percentage value that shows how frequently the rule head occurs among all the groups containing the rule body. The confidence value indicates how reliable this rule is. You set minimum confidence as part of defining mining settings. …

What are the problems of association rule mining?

Some of the main drawbacks of association rule algorithms in e-learning are: the used algorithms have too many parameters for somebody non expert in data mining and the obtained rules are far too many, most of them non-interesting and with low comprehensibility.

How do you know if an association rule is strong?

Lift value near 1 indicates X and Y almost often appear together as expected, greater than 1 means they appear together more than expected and less than 1 means they appear less than expected. Greater lift values indicate stronger association.

How do you create a strong association rule?

Finding and Making the Rules

1. Frequent Itemset Generation:- find all itemsets whose support is greater than or equal to the minimum support threshold.
2. Rule generation: generate strong association rules from the frequent itemset whose confidence greater than or equal to minimum confidence threshold.

How do you find strong association rules?

Which is an example of the association rule?

Support Count () – Frequency of occurrence of a itemset. Here ({Milk, Bread, Diaper})=2 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 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.

How is the number of transactions related to the association rule?

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.

How to use association rules in data science?

1 Support This measure gives an idea of how frequent an itemset is in all the transactions. Consider itemset1 = {bread} and itemset2 = {shampoo}. 2 Confidence This measure defines the likeliness of occurrence of consequent on the cart given that the cart already has the antecedents. 3 Lift