What is Association Rule Mining?
Learn what Association Rule Mining is and its uses.
Association rule mining is a very important technique in data mining. Association rules are ‘If-then’ statements. It helps to show the probability of relationships between data items.
In data science, association rule mining is used to find frequent patterns, correlations, associations, or causal structures from large data sets. Those data sets can be found in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories. The act of using association rules is referred to as ‘association rule mining.
We can see many real-world use cases for association rule mining. Such as medicine, retail, entertainment, user experience (UX) design, etc.
The usage of machine learning models involves analyzing data for patterns or occurrences in a database in association rule mining.
An association rule has two parts: an antecedent (if) and a consequent (then). An antecedent is an item found within the data. A consequent is an item found in combination with the antecedent.
For a quick understanding, consider the following association rule:
“If a customer buys bread, he’s 70% likely of buying butter.”
(Bread is the antecedent in the given association rule, and butter is the consequent.)
A typical example of association rule mining is a Market Based Analysis. Because of its retail origins. The Association rule shows how frequently an item set occurs in a transaction.
For retailers, association rule mining offered a way to better understand customer purchase behaviors.
Benefits of Association Rule Mining -
The strength or reliability of association rule mining is a very important thing to consider. As association rule mining finds interesting associations and relationships among large sets of data items, it shows how frequently an item set occurs in a transaction.
In data mining, association rules are useful for analyzing and predicting customer behavior.
Drawbacks of Association Rule Mining -
The primary disadvantages of association rule algorithms are obtaining boring rules, having a large number of discovered rules, and low algorithm performance. The employed algorithms contain too many parameters for someone who is not an expert in data mining, and the produced rules are too many, most of them being uninteresting and having low comprehensibility.
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