What is a segmentation modeling method that automatically sets the number of clusters into which records are then classified?

The segmentation modeling method that automatically determines the number of clusters into which records are classified is known as the TwoStep method. This approach is particularly advantageous for its ability to handle both continuous and categorical variables, making it versatile for various data types in predictive analytics.

In the TwoStep clustering process, the model first creates pre-clusters to group observations based on their similarity. This step allows for a more efficient and effective hierarchy of clusters before the final cluster assignment. Once the pre-clusters are formed, the algorithm then selects the optimal number of clusters based on the characteristics of the data. This automatic determination of clusters reduces the need for the analyst to guess or specify the number of clusters upfront, which can be a common limitation with other clustering methods.

By using statistical criteria such as the Bayesian Information Criterion (BIC) during the model fitting process, the TwoStep method ensures robustness and can reveal the underlying structure in the data more effectively.

In contrast, methods like K-Means require the analyst to specify the number of clusters beforehand, which can lead to suboptimal results if the chosen number does not reflect the actual data distribution. Kohonen networks also do not automatically determine the number of clusters but instead rely on the user to define the network parameters. Auto