The correct answer is B because K-means is an unsupervised learning algorithm used for clustering data points into groups (clusters) based on feature similarity. It is ideal for customer segmentation use cases where the goal is to discover natural groupings based on buying behavior and demographics without pre-labeled data.
From AWS documentation:
"K-means is a clustering algorithm that assigns data points to one of K groups based on feature similarity. It is commonly used in marketing and customer segmentation to group users with similar characteristics."
Explanation of other options:
A. K-nearest neighbors is a supervised classification algorithm, not intended for clustering.
C. Decision tree is also a supervised learning method used for classification or regression tasks.
D. Support vector machine is used for classification and regression, not unsupervised clustering.
Referenced AWS AI/ML Documents and Study Guides:
AWS ML Specialty Guide – Unsupervised Learning and Clustering
AWS ML Algorithm Selection Guide
Amazon SageMaker Built-in Algorithms – K-means Clustering
