DA0-002 Data Analysis Practice Question
An analyst is performing K-means clustering on customer data. The elbow method shows a clear bend at k=4. Which THREE of the following are true about K-means clustering with k=4?
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
✓
The number of clusters is determined to be 4.
K-means initialises centroids randomly, so results can vary. The elbow method suggests 4 clusters. The algorithm minimises within-cluster sum of squares. K-means works best with numeric data and assumes spherical clusters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The number of clusters is determined to be 4.
Why this is correct
Correct: Elbow method indicates k=4.
- ✗
The algorithm will always produce the same clusters regardless of initial centroids.
Why it's wrong here
Different initial centroids can lead to different final clusters.
- ✓
The centroids are recomputed iteratively until convergence.
Why this is correct
Correct: K-means iterates until centroids stabilise.
- ✗
Categorical variables should be standardised before clustering.
Why it's wrong here
Standardisation is for numeric; categorical variables need encoding, but K-means is not ideal for categorical.
- ✓
The algorithm minimises the sum of squared distances between points and their assigned centroid.
Why this is correct
Correct: That is the objective of K-means.
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