Which data mining process determines values for an unknown continuous variable?

The process that determines values for an unknown continuous variable is estimation analysis. This method is specifically designed to project outcomes based on known data points and to estimate the values of continuous variables. In data mining, estimation analysis uses algorithms to create mathematical models that can predict or estimate the value of a variable that is not readily observable but is influenced by other known variables.

This process is particularly useful in scenarios where decision-making relies on forecasts, such as predicting sales, customer behavior, or other metrics that vary continuously. By analyzing historical data and identifying patterns, estimation analysis allows organizations to make more informed decisions based on expected values.

In contrast, the other processes mentioned serve different purposes. Affinity grouping and market basket analysis focus on discovering relationships and patterns in transactional data, typically to identify associations between items purchased together. Cluster analysis, on the other hand, is used to group similar items or observations together based on their characteristics, rather than estimating unknown values. Thus, estimation analysis emerges as the clear choice for determining values for an unknown continuous variable.