MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset with mixed data types (numerical, categorical, text). The goal is to identify clusters of similar records. Which technique is most appropriate?
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-prototypes clustering
K-prototypes extends k-means to handle mixed data by combining Euclidean distance for numerical and Hamming distance for categorical. K-means only works with numerical data. DBSCAN works on numerical data. Hierarchical clustering typically uses numerical distance. Gower distance can be used but is less common in clustering algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
DBSCAN
Why it's wrong here
DBSCAN requires a distance metric that handles mixed types, which is not directly supported.
- ✗
Hierarchical clustering
Why it's wrong here
Hierarchical clustering typically uses Euclidean distance, not suitable for categorical.
- ✗
K-means clustering
Why it's wrong here
K-means requires numerical features only.
- ✓
K-prototypes clustering
Why this is correct
K-prototypes is designed for mixed numerical and categorical data.
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