What is meant by association rule?
What is meant by association rule?
Association rules are “if-then” statements, that help to show the probability of relationships between data items, within large data sets in various types of databases.
What is antecedent and consequent in association rule?
Antecedent and Consequent The IF component of an association rule is known as the antecedent. The THEN component is known as the consequent. The antecedent and the consequent are disjoint; they have no items in common.
How do you interpret confidence in association rules?
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.
How are association rules represented?
Association rules (Pang-Ning et al., 2006) are usually represented in the form X → Y, where X (also called rule antecedent) and Y (also called rule consequent) are disjoint itemsets (ie, disjoint conjunctions of features). Rule quality is usually measured by rule support and confidence.
Which statement is true of an association rule?
Which statement is true of an association rule? It is ultimately judged on how actionable it is and how well it explains the relationship between item sets.
What is lift in association rule?
The lift value of an association rule is the ratio of the confidence of the rule and the expected confidence of the rule. The expected confidence of a rule is defined as the product of the support values of the rule body and the rule head divided by the support of the rule body.
How do you interpret lift in association rules?
How to interpret the results? For an association rule X ==> Y, if the lift is equal to 1, it means that X and Y are independent. If the lift is higher than 1, it means that X and Y are positively correlated. If the lift is lower than 1, it means that X and Y are negatively correlated.
What are the various kinds of association rules?
Association rules that involve two or more dimensions or predicates can be referred to as multidimensional association rules. Rule above contains three predicates (age, occupation, and buys), each of which occurs only once in the rule. Hence, we say that it has no repeated predicates.
What is Association in unsupervised learning?
Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset.
What are the various kinds of association rules in data mining?
Association rules generated from mining data at multiple levels of abstraction are called multiple-level or multilevel association rules. Multilevel association rules can be mined efficiently using concept hierarchies under a support-confidence framework.
What are the steps involved in association rule mining process?
Association rule generation is usually split up into two separate steps: First, minimum support is applied to find all frequent itemsets in a database. Second, these frequent itemsets and the minimum confidence constraint are used to form rules.
