Association Rule Mining
Association Rule Mining is a data mining technique used to discover interesting relationships, patterns, or associations between items in large datasets, often in transactional databases. It identifies rules that describe how items are frequently purchased or used together, such as in market basket analysis. The most common algorithm for this is the Apriori algorithm, which uses measures like support, confidence, and lift to evaluate the strength of associations.
Developers should learn Association Rule Mining when working on recommendation systems, retail analytics, or any application requiring pattern discovery in transactional data, such as e-commerce platforms to suggest related products. It is also useful in fields like healthcare for identifying symptom correlations or in web usage mining to analyze user behavior. This technique helps uncover hidden insights that can drive business decisions and improve user experiences.