Let's break down why:
Why K-means is correct:
The company wants to find "groups" of customers → This indicates a clustering task
K-means is specifically designed for grouping/clustering similar data points
It works well with multiple features (demographics AND buying patterns)
K-means can automatically discover natural groupings in customer data
It's commonly used for customer segmentation in business applications
Why other options are incorrect:
A (K-nearest neighbors): This is for classification when you already have labeled data, not for discovering groups
K-means is a clustering algorithm that groups data points into clusters based on their similarities. It is particularly well-suited for unsupervised learning tasks where the goal is to identify natural groupings within the data, such as segmenting customers based on demographics and buying patterns.
Answer: B. K-means
The company should use K-means to group customers based on demographics and buying patterns. K-means is an unsupervised clustering algorithm that effectively partitions data into natural groups, making it ideal for discovering customer segments without prior labeling.
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