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
Gower distance can handle mixed data types by computing a dissimilarity matrix that combines numeric and categorical attributes.
- ✗
K-modes clustering
Why it's wrong here
K-modes is for categorical data only; it does not handle numeric variables.
- ✗
DBSCAN with Euclidean distance
Why it's wrong here
DBSCAN with Euclidean distance is designed for numeric data; it cannot handle categorical variables without transformation.
- ✗
K-means clustering
Why it's wrong here
K-means requires numeric data and uses Euclidean distance, which is not suitable for categorical variables.
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