BenchMark Quizzes by MDFT Pro

Identify Required Data Type For K-Means Clustering Features

Mark, the Student Segmentation Analyst at MDFT Pro, is building a K-means clustering model to group students based on their learning characteristics and course performance metrics. MDFT Pro, a well-known training agency, has collected diverse data about students including numerical scores (assessment grades, completion rates), categorical information (course levels, learning preferences), and text feedback (written evaluations). Before training the clustering model, Mark needs to ensure all the input features are properly formatted and compatible with the K-means algorithm’s mathematical requirements for calculating distances between student profiles.

What data type requirement must all features meet when building a K-means clustering model for student segmentation?

Choose the correct answer from the options below.

Explanations for each answer:

Boolean is incorrect. Boolean values can be used but must be converted to numeric format (0/1) since K-means requires numeric data for distance calculations.
Integer is incorrect. While integers are numeric, K-means works with all numeric types including decimals, not just integers specifically.
Numeric is correct. K-means clustering requires all features to be numeric because it calculates distances between data points using mathematical operations that only work with numbers.
Text is incorrect. Text data cannot be used directly in K-means clustering and must be converted to numeric representations like word embeddings or encoded values.

Learn more about K-means clustering:

K-Means Clustering

Exam Preparation Quiz

Identify Required Data Type For K-Means Clustering Features