Association rule learning
Ran Yan, Shuaian Wang · 2022
Association rule learning is a rule-based and unsupervised machine learning method, which is widely used to discover interesting relations between items (i.e., records) in a database and also to explore how and why these items are connected. A widely known application of association rule learning is to analyze the purchased items in market basket transactions, which aims to identify what goods are often bought together in one transaction so as to adjust sales strategies to increase sales. For example, a famous story is that on Friday nights, the sales of diapers and beer were correlated in Walmart: a bottle of beer was often bought when diapers were bought. The explanation was that working men were asked to pick up diapers on their way home from work, and they would also buy a bottle of beer for themselves at the same time. Based on this finding, Walmart put the shelves of these two goods close to each other on Friday nights, and the sales volume of both increased greatly. In the above example, diapers and beer are correlated in the following way: diapers → beer, meaning that if diapers are bought, beer is highly like to be bought in the same transaction. This is a basic form of association rule we are going to cover in this section. Actually, association rule learning is widely used to mine the relations of items from transaction databases, and the rules generated are used to guide the activities of personalized product recommendation, shelf placement, combined coupon dispatching, and bundle sales in the retailing industry.