AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Partition-based clustering is distinguished by its objective to divide data into distinct, non-overlapping groups or clusters. This typically implies that the formed clusters are in the shape of spheres or convex forms, particularly in popular algorithms like K-means, where the goal is to minimize the variance within each cluster.

This spherical nature arises because the distance measurement used (commonly Euclidean distance) tends to create clusters centered around centroids, leading to clusters that are more or less circular, especially in multi-dimensional space. The clustering paradigm does not inherently account for irregular shapes, which is unlike some density-based clustering algorithms that can form clusters of various shapes based on data density.

The other characteristics mentioned in the other choices do not uniquely define partition-based clustering. While it is often faster for larger datasets, this attribute can also apply to certain hierarchical or density-based methods that are optimized for speed. The assertion about prior knowledge of the number of clusters being unnecessary aligns more with density-based methods that can determine clusters based on data distribution. Additionally, connectivity of samples is a defining characteristic of hierarchical clustering approaches. Thus, the focus on spherical clusters is what sets partition-based algorithms apart.