DA0-002 Data Analysis Practice Question
A marketing team wants to segment customers into distinct groups based on purchasing behavior. The data includes numeric features such as frequency, monetary value, and recency. Which unsupervised learning algorithm should be used?
⚠ Common exam trap
Many candidates confuse unsupervised clustering (K-means) with supervised classification (decision tree) or regression (linear regression), mistakenly thinking any algorithm that 'groups' data must be supervised, or that association rules are for segmentation rather than transaction pattern mining.
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
✓
K-means clustering
K-means clustering is the correct choice because it is an unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity. For segmenting customers by purchasing behavior (frequency, monetary value, recency), K-means groups customers with similar numeric patterns without requiring labeled outcomes, making it ideal for exploratory segmentation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Decision tree
Why it's wrong here
Decision trees are supervised and used for classification or regression, not unsupervised segmentation.
- ✓
K-means clustering
Why this is correct
K-means is an unsupervised clustering algorithm suitable for grouping customers based on numeric attributes.
- ✗
Linear regression
Why it's wrong here
Linear regression is a supervised learning algorithm for prediction, not segmentation.
- ✗
Association rules
Why it's wrong here
Association rules are used to find frequent itemsets, not for clustering continuous features.
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