What is association rule in statistics?

Association rules is a method of data mining . The idea is to find a statistical association between some items in a large set of items, e.g. items purchased in a supermarket by a customer in one visit.

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 association rule in data mining with example?

Association rule mining is a procedure which is meant to find frequent patterns, correlations, associations, or causal structures from data sets found in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.

What are association rules in machine 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.

Is association rule supervised or unsupervised?

Association rule is unsupervised learning where algorithm tries to learn without a teacher as data are not labelled. Association rule is descriptive not the predictive method, generally used to discover interesting relationship hidden in large datasets.

What are the various kinds of association rules?

Types of Association Rules
  • Multi-relational association rules.
  • Generalized association rules.
  • Quantitative association rules.
  • Interval information association rules.

What is association rule learning how does association rule learning work also define its types and application?

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.

What is association rule mining output?

3 Association rules. ARM is a data mining method for identifying all associations and correlations between attribute values. The output is a set of association rules that are used to represent patterns of attributes that are frequently associated together (ie, frequent patterns).

How do you evaluate association rules?

Evaluating Association Rules

Minimum support and confidence are used to influence the build of an association model. Support and confidence are also the primary metrics for evaluating the quality of the rules generated by the model. Additionally, Oracle Data Mining supports lift for association rules.

What is association rule mining explain Apriori algorithm?

The Apriori algorithm uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected.

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.

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.