Decision trees and Bayesian classifiers are examples of classification algorithms. Regression - learn a function which can describe data with real values as the output range. A car salesperson might want to know how many cars she can expect to sell the next month. Linear regression and neural networks are the most used regression methods. A mail-order firm might for instance want to classify its customers into groups that are likely to purchase different items.
Thorough investigation is required to understand the meaning of the clusters.
An example of clustering is the minimax algorithm. Dependency modeling - find structural or quantitative dependencies between features. A dairy might want to know whether there is a dependency between the sales of milk and milkshake products, or if the latter compete with other products. Association rules and Rough Sets are examples of dependency models. Change and deviation analysis - find changes in a database, by comparing with previously measured or normative values.