What technique aims to minimize within-class variance and maximize between-class variance?

The technique that aims to minimize within-class variance and maximize between-class variance is known as Natural Breaks. This method is particularly effective for classification in GIS and statistical data analysis. It works by identifying natural groupings in data based on the underlying distribution of values, which allows for the formation of classes that are distinct from one another.

Natural Breaks uses an algorithm that evaluates the variance of data points within classes and between classes. By minimizing the variance within each class, the method ensures that items are as similar as possible to one another. At the same time, by maximizing the variance between the created classes, it ensures that these classes are as different from each other as possible. This approach is particularly useful when the data has inherent patterns or clusters, allowing for a more meaningful representation of the data when visualized or analyzed.

Other techniques, while useful in various contexts, do not specifically target the minimization of within-class variance and maximization of between-class variance in the same way. For instance, K-means clustering also seeks to minimize variance but does so through an iterative process that may not necessarily respect the natural distribution of data.