What type of clustering is suitable for larger datasets with categorical variables?

Two-step clustering is particularly suitable for larger datasets with categorical variables because it is specifically designed to handle mixed data types, including both categorical and continuous variables. This method effectively handles large datasets by first creating pre-clusters based on categorical data and then refining those into larger clusters. This approach allows for efficient processing and can manage complex relationships within the data.

Hierarchical clustering and K-means clustering are less effective for larger datasets, as hierarchical clustering can be computationally intensive and struggles with scalability, while K-means requires numerical values and is not well-suited for categorical data without proper encoding. Stratification clustering focuses on arranging data into strata, which may not provide the comprehensive clustering needed for larger datasets that include categorical variables.