Which type of analysis identifies rules based on combinations of items?

The type of analysis that identifies rules based on combinations of items is Market Basket analysis. This method is primarily used in retail to understand how items are grouped together in transactions. By examining past transactions, Market Basket analysis uncovers patterns that reveal which products customers are likely to purchase together. For instance, if many customers who buy bread also tend to buy butter, this relationship can be defined as a rule.

Market Basket analysis employs techniques such as association rules and frequent itemset mining. The goal is to find associations between different items that customers purchase, enabling businesses to make informed decisions about product placement, promotions, and inventory management based on those observed relationships.

In contrast, clustering focuses on grouping similar items or individuals together based on features, but it does not specifically analyze item combinations in transactions. Predictive analysis aims to forecast future outcomes based on historical data, which involves modeling and prediction rather than examining item relationships directly. Sequence analysis deals with understanding patterns in sequential data, such as the order of events or transactions over time, rather than combinations in a single transaction.