DA0-002 Data Analysis Practice Question
A company’s marketing team wants to segment customers based on purchase history, demographics, and website behavior. The data includes both numeric and categorical variables. Which clustering algorithm is best suited for handling mixed data types?
⚠ Common exam trap
A common mix-up: candidates assume K-means or DBSCAN can handle mixed data by simply encoding categorical variables, but they overlook that Euclidean distance on encoded data distorts the geometry and fails to preserve the natural dissimilarity structure of categorical variables.
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
✓
Hierarchical clustering with Gower distance
Hierarchical clustering with Gower distance is best suited for mixed data types because Gower distance computes a dissimilarity measure that handles both numeric and categorical variables by normalizing numeric differences and using a simple matching coefficient for categorical ones. This allows the algorithm to create a distance matrix that equally weights all variable types, making it ideal for segmenting customers with purchase history, demographics, and website behavior data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Hierarchical clustering with Gower distance
Why this is correct
Hierarchical clustering with Gower distance computes pairwise dissimilarity across numeric and categorical attributes simultaneously, so mixed-type records can be segmented without arbitrary encoding. It satisfies the stem's mixed data constraint, unlike k-means, which relies on Euclidean distance and requires numeric, scaled input.
- ✗
K-modes clustering
Why it's wrong here
K-modes handles only categorical variables via modal dissimilarity; it discards the numeric purchase-history and behavioural measures the scenario requires. It would be correct for segmenting purely categorical survey responses, but here it cannot process the numeric half of the dataset.
- ✗
DBSCAN with Euclidean distance
Why it's wrong here
Euclidean distance assumes continuous, ordered features, so it cannot quantify categorical values such as demographics or page categories without arbitrary encoding that distorts cluster structure. DBSCAN suits spatial, density-based outlier detection over purely numeric coordinates, not mixed-type customer segmentation.
- ✗
K-means clustering
Why it's wrong here
K-means computes means and Euclidean distances, which are undefined for categorical variables; encoding them creates false ordering and meaningless centroids. It would be correct for clustering purely numeric data such as continuous spend or session duration, not mixed demographic and behavioural attributes.
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