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DA0-002 Data Analysis Practice Question

A data scientist is analyzing a dataset with multiple features and wants to apply k-means clustering to segment customers. She chooses k = 4 based on the elbow method. During the iteration process, which of the following correctly describes a step in the k-means algorithm?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Assign each point to the nearest centroid based on Euclidean distance, then update centroids as the mean of points in each cluster.

K-means iteratively assigns each point to the nearest centroid, then recalculates centroids as the mean of points in the cluster.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Compute the covariance matrix and use principal components to initialize centroids.

    Why it's wrong here

    This is not part of the standard k-means algorithm.

  • Use hierarchical clustering to determine initial centroids.

    Why it's wrong here

    K-means typically uses random initialization; hierarchical is separate.

  • Randomly assign centroids and then compute distances to the cluster medians.

    Why it's wrong here

    K-means uses means, not medians.

  • Assign each point to the nearest centroid based on Euclidean distance, then update centroids as the mean of points in each cluster.

    Why this is correct

    This is the standard k-means iteration.

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