# Basics (Twostep-AS Cluster)

## Number of Clusters

## Clustering Criterion

This selection controls how the automatic clustering algorithm determines the number of clusters.

- Bayesian Information Criterion (BIC)
- A measure for selecting and comparing models based on the -2 log likelihood. Smaller values indicate better models. The BIC also "penalizes" overparameterized models (complex models with a large number of inputs, for example), but more strictly than the AIC.
- Akaike Information Criterion (AIC)
- A measure for selecting and comparing models based on the -2 log likelihood. Smaller values indicate better models. The AIC "penalizes" overparameterized models (complex models with a large number of inputs, for example).

## Automatic Clustering Method

If you select Determine automatically, choose from the following clustering methods used to automatically determine the number of clusters:

- Use Clustering Criterion setting
- Information criteria convergence is the ratio of information criteria corresponding to two current cluster solutions and the first cluster solution. The criterion used is the one selected in the Clustering Criterion group.
- Distance jump
- Distance jump is the ratio of distances corresponding to two consecutive cluster solutions.
- Maximum
- Combine results from the information criteria convergence method and the distance jump method to produce the number of clusters corresponding to the second jump.
- Minimum
- Combine results from the information criteria convergence method and the distance jump method to produce the number of clusters corresponding to the first jump.

## Feature Importance Method

Feature Importance Method determines how important the features (fields) are in the cluster solution. The output includes information about overall feature importance and the importance of each feature field in each cluster. Features that do not meet a minimum threshold are excluded.