If an enterprise has a large amount of unlabeled sales data, which models are suitable for identifying VIP customers?

When dealing with a large amount of unlabeled sales data, the goal is often to classify or cluster the data to identify specific segments of customers, such as VIP customers. Hierarchical clustering and K-means are both unsupervised learning techniques designed specifically for this purpose.

Hierarchical clustering groups data points into a tree-like structure based on their similarity, enabling you to observe how customers are related to each other. This method can reveal natural clusters within the data without needing prior labels. K-means clustering, on the other hand, partitions the data into k distinct clusters based on feature similarity, allowing the enterprise to segment customers effectively based on their purchasing patterns or behavior. This can help identify which customers might be classified as VIPs based on their sales volume or frequency of purchase.

The other options mainly include supervised learning techniques or models that require labeled data for training, which goes against the context of having unlabeled data. Therefore, using hierarchical clustering and K-means for clustering analysis is the most suitable approach in this scenario to uncover insights about VIP customers based purely on the available data.