Which of the following is a key difference between KNN and k-means machine-learning techniques?

→ K-Nearest Neighbors (KNN) is a supervised machine learning algorithm used primarily for classification and regression. It labels a new instance by majority vote (or averaging, in regression) of its k-nearest labeled neighbors.

→ k-Means is an unsupervised learning algorithm used for clustering. It partitions unlabeled data into k groups based on feature similarity, using centroids.

Thus, the key difference is in their purpose:

KNN → Classification (Supervised)

K-Means → Clustering (Unsupervised)

Why the other options are incorrect:

A: Both can technically operate on continuous or categorical data (with preprocessing).

B: This is not a meaningful or standardized distinction.

C: This reverses the actual roles. k-means finds centroids; KNN finds nearest neighbors.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.1 (Classification vs. Clustering):“KNN is a supervised learning algorithm for classification tasks. K-means is an unsupervised clustering technique that groups data by proximity to centroids.”

Data Science Handbook, Chapter 5:“One key distinction: KNN uses labeled data to classify or regress; k-means uses unlabeled data to identify groupings.”

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