DA0-002 Data Analysis Practice Question
In a dataset with variables on different scales (e.g., age in years and income in dollars), which preprocessing step is necessary before applying k-means clustering?
⚠ Common exam trap
Watch out — candidates often confuse normalization with other preprocessing steps like feature selection or dimensionality reduction, thinking that removing irrelevant features or reducing dimensions will automatically fix scale differences, but k-means specifically requires scaling to ensure equal feature influence in distance calculations.
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
✓
Normalization (scaling)
K-means clustering relies on Euclidean distance to measure similarity between data points. When variables like age (in years) and income (in dollars) are on different scales, the variable with larger numeric values (income) will dominate the distance calculation, skewing the clustering results. Normalization (scaling), such as min-max scaling or z-score standardization, rescales all features to a comparable range (e.g., [0,1] or mean=0, variance=1), ensuring each feature contributes equally to the distance computation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature selection
Why it's wrong here
Feature selection reduces predictors but does not address scale differences.
- ✗
Dimensionality reduction
Why it's wrong here
Dimensionality reduction may help but is not necessary for scale issues.
- ✓
Normalization (scaling)
Why this is correct
Normalization ensures each feature contributes equally to distance calculations.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding transforms categorical variables, not scales continuous ones.
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