DA0-002 Data Analysis Practice Question
A marketing analyst wants to segment customers based on their purchase history, including total spent, number of transactions, and average order value. The analyst runs k-means clustering with k=5 on the raw data but notices that the cluster assignments change significantly every time the algorithm is executed. What should the analyst do first to obtain consistent and meaningful clusters?
⚠ Common exam trap
Watch out — candidates often think the instability is due to the choice of k or the algorithm itself, rather than recognizing that k-means is sensitive to feature scaling and random initialization, which are the first things to address for consistency.
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
✓
Normalize the features and set a fixed random seed for the initial centroids.
The instability in cluster assignments is caused by the algorithm's sensitivity to the scale of features and the random initialization of centroids. Normalizing the features ensures that each variable contributes equally to the distance calculations, while setting a fixed random seed makes the initial centroid selection deterministic, leading to reproducible results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Normalize the features and set a fixed random seed for the initial centroids.
Why this is correct
Normalization ensures all features contribute equally, and a fixed seed ensures reproducible results.
- ✗
Switch to hierarchical clustering, which does not require specifying k.
Why it's wrong here
Hierarchical clustering also has parameters; the immediate issue is scaling and initialization, not the clustering algorithm itself.
- ✗
Increase the number of clusters to k=10 to capture more detail.
Why it's wrong here
Increasing k may not address instability; it might even increase variability.
- ✗
Use principal component analysis (PCA) to reduce the number of features to two.
Why it's wrong here
PCA can help with dimensionality but does not address the instability caused by random initialization and unscaled data.
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