A data record might, for example, consist of information about a customer. The Clustering algorithm groups similar customers together. At the same time it maximizes the differences between the different customer groups that are formed in this way.
The groups that are found are known as clusters. Each cluster tells a specific story about customer identity or behavior, for example, about their demographic background, or about their preferred products or product combinations. In this way, customers that are similar are grouped together in homogeneous groups. These groups are then available for marketing or for other business processes.
The algorithms of the Clustering mining function provide common parameters and algorithm-specific parameters.