Association rule learning is an unsupervised learning technique that examines the dependency of one data item on another and maps accordingly to make it more profitable. It tries to discover some interesting relationships or links between the dataset's variables.
Association Rules Come in a Variety of Forms:
Rules for multi-relational association.
Association norms that are universal.
Quantitative connection is the only way to go.
Information association rules based on intervals.
The challenge of uncovering intriguing correlations in vast datasets is known as association analysis. There are two types of interesting relationships: frequent item sets and association rules. A frequent item set is a group of objects that appear frequently together.
An itemset is a collection of zero or more items in association analysis. A k-itemset is an itemset that has k items in it. A 3-itemset, for example, might be Beer, Diapers, and Milk. A null (or empty) set is an itemset that has no items in it.